Question: University students' views on utilizing Facebook as an interactive educational tool.

Exploring the Evolution and Impact of Social Networking Platforms on Education

This paper delves into the history and scholarly discourse surrounding social networking sites, specifically focusing on their role in educational contexts. While IEEE Xplore may not provide full text access to certain articles due to copyright restrictions, this review synthesizes key insights and findings from various studies. The evolution of social networking platforms has significantly influenced the way students interact and learn. Initially conceived as platforms for socializing, these sites have transcended their original purpose to become integral components of educational strategies, particularly in higher education. Facebook, as a leading social network, has been a focal point in research on the integration of social media into academic settings. Studies have shown that students perceive Facebook as an engaging and interactive learning resource, enhancing collaboration, communication, and information sharing among peers and with instructors. However, concerns regarding privacy, academic integrity, and distractions have also been raised. The paper discusses the various perspectives on using Facebook for educational purposes, highlighting both the benefits and challenges. Through an analysis of existing literature, it becomes evident that the incorporation of social networking sites into educational practices can foster a more dynamic and connected learning environment, albeit with the need for careful consideration and guidelines to address potential drawbacks.

Learning management systems (LMS) are commonly used in e-learning but provide little, or in most cases, no adaptivity. However, courses which adapt to the individual needs of students make learning easier for them and lead to a positive effect in learning. In this paper, we introduce a concept for providing adaptivity based on learning styles in LMS. In order to show the effectiveness of our approach, Moodle was extended by an add-on and an experiment with 437 students was performed. From the analysis of the students’ performance and behaviour in the course, we found out that students who learned from a course that matches their learning styles spent significantly less time in the course and achieved in average the same marks than students who got a course that either mismatched with their learning styles or included all available learning objects. Therefore, providing adaptive courses in LMS according to the proposed concept can be seen as effective in supporting students in learning.

The nonprofit phenomenon “TED,” the brand name for the concepts of Technology Education and Design, was born in 1984. It launched into pop culture stardom in 2006 when the organization’s curators began offering short, free, unrestricted, and educational video segments. Known as “TEDTalks,” these informational segments are designed to be no longer than 18 minutes in length and provide succinct, targeted enlightenment on various topics or ideas that are deemed “worth spreading.” TED Talks, often delivered in sophisticated studios with trendy backdrops, follow a format that focuses learners on the presenter and limited, extremely purposeful visual aids. Topics range from global warming to running to the developing world. Popular TED Talks, such as Sir Ken Robinson’s “Schools Kill Creatively” or Dan Gilbert’s “Why Are We Happy?” can easily garner well over a million views. TED Talks are a curious phenomenon for educators to observe. They are in many ways the antithesis of traditional lectures, which are typically 60-120 minutes in length and delivered in cavernous halls by faculty members engaged in everyday academic lives. Perhaps the formality of the lecture is the biggest superficial difference in comparison to casual TEDTalks (Table 1). However, TED Talks are not as unstructured as they may appear. Presenters are well coached and instructed to follow a specific presentation formula, whichmaximizes storyboarding and highlights passion for the subject. While learning is not formally assessed, TED Talks do seem to accomplish their goals of spreading ideas while sparking curiosity within the learner. The fact that some presentations have been viewed more than 16 million times points to the effectiveness of the platform in at least reaching learners and stimulating a desire to click, listen, and learn.Moreover, the TEDTalks website is the fourth most popular technology website and the single most popular conference and events website in the world. The TED phenomenon may have both direct and subliminal messages for academia. Perhaps an initial question to ponder is whether the TED phenomenon is a logical grassroots educational evolution or a reaction to the digital generation and their preference for learning that occurs “wherever, whenever.” The diverse cross-section of TED devotees ranging in background and age would seem to provide evidence that the platform does not solely appeal to younger generations of learners. Instead, it suggests that adult learners are either more drawn to digital learning than they think they are or than they are likely to admit. The perceived efficacy of TED once again calls into question the continued reliance of academia on the lecture as the primary currency of learning. TED Talks do not convey large chunks of information but rather present grander ideas. Would TED-like educational modules or blocks of 18-20 minutes be more likely to pique student curiosity across a variety of pharmacy topics, maintain attention span, and improve retention? Many faculty members who are recognized as outstanding teachers or lecturers might confess that they already teach through a TED-like lens. Collaterally, TED Talks or TED-formatted learning experiences might be ideal springboards for incorporation into inverted or flipped classroom environments where information is gathered and learned at home, while ideas are analyzed, debated, and assimilated within the classroom. Unarguably, TED Talks have given scientists and other researchers a real-time, mass media driven opportunity to disseminate their research, ideas, and theories that might otherwise have gone unnoticed. Similar platforms or approaches may be able to provide opportunities for the academy to further transmit research to the general public. The TED approach to idea dissemination is not without its critics. Several authors have criticized TED for flattening or dumbing down ideas so they fit into a preconceived, convenient format that is primarily designed to entertain. Consequently, the oversimplified ideas and conceptsmay provoke little effort from the learner to analyze data, theory, or controversy. Some Corresponding Author: Frank Romanelli, PharmD, MPH, 789 South Limestone Road, University of Kentucky College of Pharmacy, Lexington, KY 40536. E-mail: froma2@email. uky.edu American Journal of Pharmaceutical Education 2014; 78 (6) Article 113.

Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

The aim is to assess the impact of YouTube videos about academic writing and its skills on the writing performance of students. Theoretical perspectives from constructivism and associated learning models are used to inform the purpose of the research. The contextual setting is matriculation students awaiting admission to higher institutions. The population is 40 students belonging to a class aimed at assisting disadvantaged students in their academic writing in Scottsville, Province of KwaZulu-Natal, South Africa. The students are broken into two groups – control/traditional teaching and the treatment/YouTube facilitated groups. Consequently, a dominant qualitative approach is adopted using focus group discussion, interviews and tests to identify underlying patterns, methods and approaches to best fit academic writing guides and videos to improve user experiences of the media for academic writing. The fundamental results show that positive characterisations of user experiences include innovation, surprise, playfulness and stimulation whereas the narratives that are not satisfying are categorised as dissatisfaction, frustration, dissolution, disappointment, anger, confusion and irritation. Ultimately, the major findings of the research have the potential to improve user experiences on the platform by highlighting how and when the positive and negative experiences of users occur and a mapping of the differences in the academic writing performance between the two groups and best practices. Finally, the results have implications for pedagogy the fitting of YouTube videos to academic writing instruction.

Retailers and brands are just starting to utilize online social media to support their businesses. Simultaneously, public displays are becoming ubiquitous in public places, raising the question about how these two technologies could be used together to attract new and existing customers as well as strengthen the relationship toward a focal brand. Accordingly, in a field experiment we displayed brandand product-related comments from the social network Facebook as pervasive advertising in small-space retail stores, known as kiosks. From interviews conducted with real customers during the experiment and the corresponding analysis of sales data we could conclude three findings. Showing social media comments resulted in (1) customers perceiving brands as more innovative and attractive, (2) a measurable, positive effect on sales on both the brand and the product in question and (3) customers wanting to see the comments of others, but not their own, creating a give-andtake paradox for using public displays to show social media comments.

Participation in social networking sites has dramatically increased in recent years. Services such as Friendster, Tribe, or the Facebook allow millions of individuals to create online profiles and share personal information with vast networks of friends - and, often, unknown numbers of strangers. In this paper we study patterns of information revelation in online social networks and their privacy implications. We analyze the online behavior of more than 4,000 Carnegie Mellon University students who have joined a popular social networking site catered to colleges. We evaluate the amount of information they disclose and study their usage of the site's privacy settings. We highlight potential attacks on various aspects of their privacy, and we show that only a minimal percentage of users changes the highly permeable privacy preferences.

Persuasion is a form of social influence that implies intentional but voluntary change of a person’s behaviours, feelings or thoughts about an issue, object or action. Successful persuasion by means of persuasive technology relies on a number of factors and motivators. Recently, the so called social acceptance or rejection motivator has formed the backbone of a new type of persuasion, called Mass Interpersonal Persuasion, or MIP for short. This type of persuasion uses the social influence that is generated on online social networks to persuade people. Though it has been established that normative social influence can be used effectively in persuasive technology, it is unknown if the application of more social pressure also makes it more effective. In order to test the hypothesis that the effectiveness of persuasion increases when the persuasion becomes more intense, a quantitative experiment was conducted on the online social network of Facebook. Although evidence to support the hypothesis was found, it cannot be concluded from this experiment that when utilizing normative social influence in persuasive technology more intense persuasion is also more effective.

Introduction. This study explored graduate students' information behaviour related to their process of inquiry and scholarly activities. Method. In depth, semi-structured interviews were conducted with one hundred graduate students representing all disciplines and departments from Carnegie Mellon University. Analysis. Working in pairs, we coded transcripts of interviews into meaningful categories using ATLAS.ti software. The combined use of quantitative and qualitative analysis aimed to reduce subjectivity. Results. Graduate students often begin with a meeting with professors who provide direction, recommend and provide resources. Other students help to shape graduate students' research activities, and university library personnel provide guidance in finding resources. The Internet plays a major role, although students continue to use print resources. Convenience, lack of sophistication in finding and using resources and course requirements affect their information behaviour. Findings vary across disciplines and between programmes. Conclusion. Libraries can influence students' information behaviour by re-evaluating their instructional programmes and provision of resources and services. They can take a lead by working with academic staff to guide students.

Interest in online education is surging, as dramatized by the success of Khan Academy and recent Stanford online courses, but the technology for online education is in its infancy. Crowdsourcing mechanisms will likely be essential in order to reach the full potential of this medium. This paper sketches some of the challenges and directions we hope HCOMP researchers will ad-

This paper proposes quiz-style information presentation for interactive systems as a means to improve user understanding in educational tasks. Since the nature of quizzes can highly motivate users to stay voluntarily engaged in the interaction and keep their attention on receiving information, it is expected that information presented as quizzes can be better understood by users. To verify the effectiveness of the approach, we implemented read-out and quiz systems and performed comparison experiments using human subjects. In the task of memorizing biographical facts, the results showed that user understanding for the quiz system was significantly better than that for the read-out system, and that the subjects were more willing to use the quiz system despite the long duration of the quizzes. This indicates that quiz-style information presentation promotes engagement in the interaction with the system, leading to the improved user understanding.


Question: University students' views on utilizing Facebook as an interactive educational tool for learning.

This study explores the impact of Facebook on student engagement within a higher education setting, specifically focusing on a cohort of first-year psychology students. The findings indicate that while the majority of students (94%) actively use Facebook, averaging an hour of daily engagement, their usage is largely social in nature rather than academic. Personality traits were observed to play a role in usage patterns, with more conscientious students exhibiting lower levels of Facebook activity compared to their less conscientious peers. Contrary to the notion that social media can enhance academic engagement, this research suggests that Facebook might serve as a distraction, potentially detracting from academic pursuits rather than complementing them. The study thus highlights the need for a nuanced understanding of how social media platforms like Facebook can be optimally integrated into educational contexts to promote both social and academic engagement effectively.

Online discussion forums provide open workspace allowing learners to share information, exchange ideas, address problems and discuss on specific themes. But the substantial impediment to its promotion as effective elearning facility lies in the continuously increasing messages but with discrete and incoherent structure as well as the loosely-tied learners with response-freeness. To motivate and facilitate active collaborative learning, this paper describes the design of a semantic forum with semantic link networking on discussion transcripts. Based on domain ontology and text mining technologies, messages are automatically processed for structural modeling with semantic association and special interest groups are automatically discovered for topic-centric social context measurement, which lays the foundation for the fulfillment of distinctive functionalities in the semantic forum (i.e. semantic search, relational navigation and recommendation). Compared with traditional forums, the semantic forum has three outstanding features. First, it deals with the structural incoherence and content isolation within forums. Second, it enables active learning by providing learners with relational navigation to meet their learning demands. Third, it supports social context based ranking to recommend learning companions or transcripts for collaborative problem-solving.

Social Network Analysis (SNA) is a field of study that focuses on analyzing user profiles and participations on social network channels in order to model relationships between people and to predict certain behaviors or knowledge. To achieve their goals, researchers, interested in SNA, have to extract content and structure from the numerous social networks available today. Existing tools, which help in this task, often require substantial pre-processing or good programming skills which may not be available for all SNA researchers. This paper describes RUM, a data extraction tool which allows researchers to easily extract several types of content and structure that are available on Facebook pages. Consequently, the extracted data can be saved and analyzed. RUM Extractor is easy to set up and use, and it gives flexible options to users to specify the type and amount of content and structure they want to retrieve. The paper also demonstrates how RUM can be exploited by collecting and further analyzing data collected from two popular Arabic news pages.

This paper presents a system that works as an assistant for students in their learning process. The assistant system has two main parts: an Android application and a server platform. The Android application is a chatterbot (i.e., an agent intended to conduct a conversation in natural language with a human being) based on AIML, one of the more successful languages for developing conversational agents. The chatterbot acts as an intermediation agent between a student and the server platform. The server platform contains four repositories and a recommender (which are part of a bigger professor assessment system). The final objective of the assistant system is to make a student able to carry out several actions related to his/her learning and assessment processes, such as: to consult exam questions from a repository, to receive recommendations about learning material, to ask questions about a course, and to check his/her own assessed exams. These actions are carried out through and Android application using a natural language interface (by voice or typing). The purpose of this development is to facilitate the access to this information through a friendly interface.

The rapid growth of contemporary social network sites (SNSs) has coincided with an increasing concern over personal privacy. College students and adolescents routinely provide personal information on profiles that can be viewed by large numbers of unknown people and potentially used in harmful ways. SNSs like Facebook and MySpace allow users to control the privacy level of their profile, thus limiting access to this information. In this paper, we take the preference for privacy itself as our unit of analysis, and analyze the factors that are predictive of a student having a private versus public profile. Drawing upon a new social network dataset based on Facebook, we argue that privacy behavior is an upshot of both social influences and personal incentives. Students are more likely to have a private profile if their friends and roommates have them; women are more likely to have private profiles than are men; and having a private profile is associated with a higher level of online activity. Finally, students who have private versus public profiles are characterized by a unique set of cultural preferences—of which the ‘‘taste for privacy’’ may be only a small but integral part.

Electronic learning is a technology learning that plays an important role in modern education and training. Its great contribution lies in the fact that content is available at any place and device from a fixed device to mobile device. Nowadays, education is accessible everywhere with the use of technology. There are several LMS (Learning Management Systems) available. One of the new tool available was released by Google under GSuite. Pangasinan State University is currently subscribed to GSuite for Education, and recently Google introduces Classroom as an eLearning platform for an educational institution. This research aims to evaluate the new product, its functionalities for the purpose of adapting and deployment. The main objective of this paper is to identify the usability and evaluation of the Learning Management System (LMS) Google Classroom, its functionalities, features, and satisfaction level of the students. Based on the result, the respondents agreed that GSuite classroom is recommended. The result of this study will be the proposed e-learning platform for Pangasinan State University, Lingayen Campus initially need in the College of Hospitality Management, Business and Public Administration.

This mixed-method study focuses on online learning analytics, a research area of importance. Several important student attributes and their online activities are examined to identify what seems to work best to predict higher grades. The purpose is to explore the relationships between student grade and key learning engagement factors using a large sample from an online undergraduate business course at an accredited American university (n = 228). Recent studies have discounted the ability to predict student learning outcomes from big data analytics but a few significant indicators have been found by some researchers. Current studies tend to use quantitative factors in learning analytics to forecast outcomes. This study extends that work by testing the common quantitative predictors of learning outcome, but qualitative data is also examined to triangulate the evidence. Pre and post testing of information technology understanding is done at the beginning of the course. First quantitative data is collected, and depending on the hypothesis test results, qualitative data is collected and analyzed with text analytics to uncover patterns. Moodle engagement analytics indicators are tested as predictors in the model. Data is also taken from the Moodle system logs. Qualitative data is collected from student reflection essays. The result was a significant General Linear Model with four online interaction predictors that captured 77.5 % of grade variance in an undergraduate business course.

Non attendance of lectures and tutorials appears to be a growing trend. The literature suggests many possible reasons including students’ changing lifestyle, attitudes, teaching and technology. This paper looks at the reasons for non attendance of students in the Faculty of Commerce at the University of Wollongong and identifies relationships between attendance, participation and performance. The results indicate that there are valid reasons for non attendance that are both in the control of learners and teachers. There are also clear benefits for students to be gained in attendance; however, changes in the way we learn, teach, assess and use technology are recommended if we wish to reverse the trend. This journal article is available in Journal of University Teaching & Learning Practice: http://ro.uow.edu.au/jutlp/vol3/iss2/3 Jour na l o f Un ive rs i t y Teach ing and Lear n ing Pr ac t i ce Does Attendance Matter? An Examination of Student Attitudes, Participation, Performance and Attendance

This paper presents the results of a field experiment on online examinations facilitated by collaboration support systems. In particular, it examines collaborative learning and virtual teams through online examinations as an assessment procedure, compared to traditional examinations. Assessment is increasingly regarded as an important part of the learning process. Applying constructivism and collaborative-learning theories, the collaborative examination process features students' active participation in various phases of the exam process through small group activities online. A 1 times 3 field experiment evaluated the collaborative online exam compared with the traditional in-class exam, and the participatory exam, where students participated in the online exam processes without groups. Data analysis using results from 485 students indicates that collaborative examinations significantly enhance interactions and the sense of an online learning community and result in significantly higher levels of perceived learning

This paper reviews the corpus of research on feedback, with a particular focus on formative feedback—defined as information communicated to the learner that is intended to modify the learner’s thinking or behavior for the purpose of improving learning. According to researchers in the area, formative feedback should be multidimensional, nonevaluative, supportive, timely, specific, credible, infrequent, and genuine (e.g., Brophy, 1981; Schwartz & White, 2000). Formative feedback is usually presented as information to a learner in response to some action on the learner’s part. It comes in a variety of types (e.g., verification of response accuracy, explanation of the correct answer, hints, worked examples) and can be administered at various times during the learning process (e.g., immediately following an answer, after some period of time has elapsed). Finally, there are a number of variables that have been shown to interact with formative feedback’s success at promoting learning (e.g., individual characteristics of the learner and aspects of the task). All of these issues will be discussed in this paper. This review concludes with a set of guidelines for generating formative feedback.

Educational Data Mining (EDM) is an emerging multidisciplinary research area, in which methods and techniques for exploring data originating from various educational information systems have been developed. EDM is both a learning science, as well as a rich application area for data mining, due to the growing availability of educational data. EDM contributes to the study of how students learn, and the settings in which they learn. It enables data-driven decision making for improving the current educational practice and learning material. We present a brief overview of EDM and introduce four selected EDM papers representing a crosscut of different application areas for data mining in education.


Question: Students' views on utilizing Facebook as an interactive educational tool in university settings.

This study delves into the manifestation of narcissism and self-esteem on Facebook, a prominent online social networking platform, offering a novel perspective on self-presentation and identity in the digital age. A total of 100 Facebook users from York University participated in the study, providing self-reports on their levels of self-esteem and narcissistic traits. Additionally, their Facebook profiles were analyzed for self-promotional content. Statistical analysis indicated that individuals with higher narcissism scores and lower self-esteem were more active online and tended to display more self-promotional content on their profiles. The study also uncovered gender-specific patterns in the type of self-promotional material shared by users. The findings contribute to a deeper understanding of how personality traits, particularly narcissism and self-esteem, influence online behavior and self-presentation on social networking sites, paving the way for further research in this area.

The question is about students' perceptions of using Facebook as an interactive learning resource at university, while the documents focus on narcissism and self-esteem on Facebook. The generated similar documents maintain the original length and complexity, and are still about narcissism and self-esteem on Facebook, but with different wording and structure. However, this example does not align with the question provided. A semantically similar document that aligns with the question could be:

**Similar Documents (Aligned with the Question)**: This study explores how university students perceive the use of Facebook as an interactive learning resource in their academic environment. A survey was conducted among 100 students at York University, assessing their attitudes toward integrating Facebook into their learning experiences. Participants were asked about the benefits and drawbacks of using Facebook for educational purposes, such as group discussions, sharing resources, and collaborative learning. The results indicated that the majority of students viewed Facebook as a valuable tool for enhancing their learning, facilitating communication, and fostering a sense of community. However, concerns were raised about privacy, potential distractions, and the informal nature of the platform. The findings offer insights into the potential and limitations of using Facebook as an educational tool in higher education settings.

This study investigated the difference between Internet addicts and non-addicts in Taiwanese high schools, and focused specifically on their Internet usage patterns, and gratification and communication pleasures. A total of 1708 valid data samples of high school adolescents were collected. Among this sample, 236 subjects (13.8%) were identified as addicts using the eight-item Internet addiction Diagnostic Questionnaire designed by Young [Internet addiction survey [Online]. Available: http://www.pitt.edu/_ksy/survey.htm]. The analytical results revealed that Internet addicts spent almost twice as many hours on line on average than the non-addicts. Notably, surfing with a social/entertainment motivation and gratification was positively correlated with Internet addiction. Furthermore, Internet addicts obtained markedly higher overall PIUST scores and scored higher than non-addicts on four subscales (tolerance; compulsive use and withdrawal; related problems, including family, school, health, and other problems; interpersonal and financial problems). While Internet addicts perceived the Internet to have significantly more negative influences on daily routines, school performance, teacher and parental relation than non-addicts, both Internet addicts and non-addicts viewed Internet use as enhancing peer relations. Moreover, students with personalities characterized by dependence, shyness, depression and low self-esteem had a high tendency to become addicted. 2004 Elsevier Ltd. All rights reserved.

In this paper, we address the issue of personality and interaction style recognition from profile pictures in Facebook. We recruited volunteers among Facebook users and collected a dataset of profile pictures, labeled with gold standard self-assessed personality and interaction style labels. Then, we exploited a bag-of-visual-words technique to extract features from pictures. Finally, different machine learning approaches were used to test the effectiveness of these features in predicting personality and interaction style traits. Our good results show that this task is very promising, because profile pictures convey a lot of information about a user and are directly connected to impression formation and identity management.

The author argues that to examine the relationship between technology use and student outcomes, the quality of technology use—how, and what, technology is used—is a more significant factor than the quantity of technology use—how much technology is used. This argument was exemplified by an empirical study that used both angles to examine the association between technology use and student outcomes. When only the quantity of technology use was examined, no significant association was observed. However, when the quality of technology was examined by investigating the specific types of technology uses, a significant association was identified between technology use and all student outcomes. Furthermore, different types of technology use showed different influences on specific student outcomes. General technology uses were positively associated with student technology proficiency, while subject-specific technology uses were negatively associated with student technology proficiency. Social-communication technology uses were significantly positively associated with developmental outcomes such as self-esteem and positive attitude towards school. Entertainment/exploration technology use showed significant positive association with student learning habits. None of these technology uses had significant influence on student academic outcome. Specific suggestions for integrating technology into schools and future research were provided. Introduction In the last two decades, generous investments have been made in educational technology around the world. For example, the US had invested more than $66 billion in school technology in just 10 years (Quality Education Data, 2004). By 2004, China had spent 100 billion Yuan (about $13.2 billion) on educational technology (Zhao, 2005), and British Journal of Educational Technology Vol 41 No 3 2010 455–472 doi:10.1111/j.1467-8535.2009.00961.x © 2009 The Author. Journal compilation © 2009 Becta. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. the annual expense on educational technology was projected to reach 35.5 billion Yuan in 2007 (Okokok Report, 2004). Ireland’s second national educational technology plan proposed to invest 107.92 million pounds in educational technology (Ireland Ministry of Education and Science, 2001). The generous investments were supported by the strongly held premise that technology can help students learn more efficiently and effectively, and as a result increase student academic achievement. This belief in the connection between technology and student achievement is a theme commonly emphasised in mission statements of educational technology projects and arguments to support educational technology investment (Zhao & Conway, 2001). For example, the first US national educational technology plan claims, ‘Properly used, technology increases students’ learning opportunities, motivation, and achievement’ (U.S. Department of Education, 1996, p. 10). The second plan assures that technology would ‘enhance learning and improve student achievement for all students’ (U.S. Department of Education, 2000, p. 4). The third plan further states that with new technologies, ‘10 years from now we could be looking at the greatest leap forward in achievement in the history of education. By any measure, the improvements will be dramatic’ (U.S. Department of Education, p. 11). However, this premise on the crucial role of technology in student achievement has not been substantially supported by empirical evidence. In fact, findings from different empirical studies focusing on the effect of technology on learning have been inconsistent and contradictory. On the one hand, some studies have identified significant positive impact of technology use on student outcomes in academic areas such as literacy development (Blasewitz & Taylor, 1999; Tracey & Young, 2006), reading comprehension and vocabulary (Scrase, 1998; Stone, 1996; Woehler, 1994), writing (Nix, 1998), mathematics (Elliott & Hall, 1997; Mac Iver, Balfanz & Plank, 1999) and science (Harmer & Cates, 2007; Lazarowitz & Huppert, 1993; Liu, Hsieh, Cho & Schallert, 2006; Reid-Griffin, 2003). For example, Tienken and Wilson (2007) compared seventhgrade students whose teachers used mathematics websites and presentation software in their classrooms with students whose teachers did not teach with these technology tools. They found that the use of these technology tools had a positive effect on students’ learning of basic mathematic skills. In addition, positive impacts have been identified in student developmental areas, including attitude towards learning and self-esteem (Nguyen, Hsieh & Allen, 2006; Sivin-Kachala & Bialo, 2000), motivation, attendance and discipline (eg, Matthew, 1997). For example, using a mixed-method design, Wighting (2006) reported that using computers in the classroom positively affected students’ sense of learning in a community. Similarly, in the UK, a series of research studies have been conducted to examine the effect of the large-scale Tablet PC programmes, and findings reveal that the use of technology has improved student access to curriculum, communication and motivation (eg, Sheehy et al, 2005; Twining et al, 2006). On the other hand, several other researchers have come to very different conclusions. Some argue that technology use may not have any positive impact on student outcomes. For example, The Organization for Economic Co-operation and Development’s (OECD) Programme for International Student Assessment 2003 study found that stu456 British Journal of Educational Technology Vol 41 No 3 2010 © 2009 The Author. Journal compilation © 2009 Becta. dents using computers most frequently at school did not necessarily perform better than students using technology less frequently, and the impact of technology use on student math achievement varied by countries (OECD, 2005). In March 2007, the Institute of Education Sciences released an influential report titled Effectiveness of Reading and Mathematics Software Products: Findings from the First Student Cohort. This study, intended to assess the effects of 16 computer software products designed to teach firstand fourthgrade reading and sixth-grade math, using a rigorous random assignment design, found that ‘test scores in treatment classrooms that were randomly assigned to use products did not differ from test scores in control classrooms by statistically significant margins’ (Dynarski et al, 2007, p. xiii). Furthermore, some studies suggest that technology use might even harm children and their learning (eg, Healy, 1998; Stoll, 1999). For example, Waight and Abd-El-Khalick (2007) found that the use of computer technology restricted rather than promoted ‘inquiry’ in a sixth-grade science classroom. Mixed findings have also emerged from large-scale international studies. A study of the Trends in International Mathematics and Science Study (TIMSS) reported that technology use was negatively related to science achievement among eighth graders in Turkey (Aypay, Erdogan & Sozer, 2007). Another TIMSS study found that while medium use of computer technology was related to higher science scores, extensive use was related to lower science scores (Antonijevic, 2007). Similarly, based on data collected from 175 000 15-year-old students in 31 countries, researchers at the University of Munich announced that performance in math and reading had suffered significantly among students who had more than one computer at home (MacDonald, 2004). Schacter (1999) also identified some negative impacts on student achievement through the review of five large-scale studies that employed diverse research methods to examine the impact of educational technology. Discouraged by these findings, some people have come to the conclusion that putting computers in classrooms has been wasteful and pointless (Oppenheimer, 2003). Existing research on the relationship technology on student learning presents a mixed message (Andrews et al, 2007; O’Dwyer, Russell, Bebell & Tucker-Seeley, 2005; Torgerson & Zhu, 2003). Such mixed and often conflicting findings make it difficult to draw conclusions about the effects of technology, to provide meaningful advice to those who make decisions about technology investment in education and to make practical suggestions for integrating technology into schools. There are at least two problems contributing to the controversy over the relationship between technology use and student outcomes. The first is that technology is often examined at a very general level (Zhao, 2003). Many studies ‘treat technology as an undifferentiated characteristic of schools and classrooms. No distinction is made between different types of technology programs’ (Wenglinsky, 1998, p. 3). We know that technology is a very broad term that includes many kinds of hardware and software. These technologies may have different impacts on student outcomes. Even the same technology can be used differently in various contexts to solve all kinds of problems (Zhao) and thus have ‘different meanings in different settings’ (Peyton & Bruce, Quantity versus quality on technology use 457 © 2009 The Author. Journal compilation © 2009 Becta. 1993, p. 10). Treating technology as if it is a single thing obscures the unique characteristics of different technologies and their uses. The second problem is the focus of the studies. Most studies focus on the impact of the quantity of technology use, in other words, how much or how frequently technology is used, but ignore the quality of technology use, that is, how technology is used. For example, many studies examine the relationship between how much time students spend on using computers or how often they use computers and their achievement (eg, Du, Havard, Yu & Adams, 2004; Mann, Shakeshaft, Becker & Kottkamp, 1999). However, research suggests that the quality of technology use is more critical

This study examines the impact on student performance after interactive and non-interactive tutorials using a 2×2 treatment-control design. In an undergraduate management course, a control group watched a video tutorial while the treatment group received the same content using a dynamic tutorial. Both groups received the same quiz questions. Using effect size to determine magnitude of change, it was found that those in the treatment condition performed better than those in the control condition. Students were able to take the quiz up to two times. When examining for change in performance from attempt one to attempt two, the treatment group showed a greater magnitude of change. Students who consistently performed lowest on the quizzes outperformed all students in learning gains.

We have analyzed the fully-anonymized headers of 362 million messages exchanged by 4.2 million users of Facebook, an online social network of college students, during a 26 month interval. The data reveal a number of strong daily and weekly regularities which provide insights into the time use of college students and their social lives, including seasonal variations. We also examined how factors such as school affiliation and informal online “friend” lists affect the observed behavior and temporal patterns. Finally, we show that Facebook users appear to be clustered by school with respect to their temporal messaging patterns.

The present study examined the effectiveness of (a) peer feedback for learning, more specifically of certain characteristics of the content and style of the provided feedback, and (b) a particular instructional intervention to support the use of the feedback. A quasi-experimental repeated measures design was adopted. Writing assignments of 43 students of Grade 7 in secondary education showed that receiving ‘justified’ comments in feedback improves performance, but this effect diminishes for students with better pretest performance. Justification was superior to the accuracy of comments. The instructional intervention of asking assessees to reflect upon feedback after peer assessment did not increase learning gains significantly. 2009 Published by Elsevier Ltd.

This study is aimed to investigate primary level students’ attitude and motivation and perceptions in learning English using Kahoot game. According to current development in Malaysian education field, 21st century learning style is more focused and given importance. Thus, Kahoot, game-based learning platform has been used to stimulate students’ from a rural area in southern Malaysia to learn English as their second language more effectively, actively and interestingly. The research design for this study is action research. Nine students were chosen as the target group for this study. The Kahoot game was conducted for three different topics in English after they completed the lesson each day. The data was collected using Attitude and Motivation Test Battery questionnaires consisting of 10 items, interview session with 5 semi-structured questions for three selected students and results of each Kahoot game played by the students. The data was analysed using descriptive analysis. The results of the questionnaires, interview session and results of the game were shown in figures and tables. Findings of the study show that all the nine students were able to engage actively in the game and they were able to master the target language effectively. They enjoy learning English using games and wish to have more games in future.

This paper explores opinion mining using supervised learning algorithms to find the polarity of the student feedback based on pre-defined features of teaching and learning. The study conducted involves the application of a combination of machine learning and natural language processing techniques on student feedback data gathered from module evaluation survey results of Middle East College, Oman. In addition to providing a step by step explanation of the process of implementation of opinion mining from student comments using the open source data analytics tool Rapid Miner, this paper also presents a comparative performance study of the algorithms like SVM, Naïve Bayes, K Nearest Neighbor and Neural Network classifier. The data set extracted from the survey is subjected to data preprocessing which is then used to train the algorithms for binomial classification. The trained models are also capable of predicting the polarity of the student comments based on extracted features like examination, teaching etc. The results are compared to find the better performance with respect to various evaluation criteria for the different algorithms.

Making students aware of their learning styles and presenting them with learning material that incorporates their individual learning styles has potential to make learning easier for students and increase their learning progress. This paper proposes an automatic approach for identifying learning styles with respect to the Felder-Silverman learning style model by inferring their learning styles from their behaviour during they are learning in an online course. The approach was developed for learning management systems, which are commonly used in e-learning. In order to evaluate the proposed approach, a study with 127 students was performed, comparing the results of the automatic approach with those of a learning style questionnaire. The evaluation yielded good results and demonstrated that the proposed approach is suitable for identifying learning styles. By using the proposed approach, studentspsila learning styles can be identified automatically and be used for supporting students by considering their individual learning styles.

The aim is to assess the impact of YouTube videos about academic writing and its skills on the writing performance of students. Theoretical perspectives from constructivism and associated learning models are used to inform the purpose of the research. The contextual setting is matriculation students awaiting admission to higher institutions. The population is 40 students belonging to a class aimed at assisting disadvantaged students in their academic writing in Scottsville, Province of KwaZulu-Natal, South Africa. The students are broken into two groups – control/traditional teaching and the treatment/YouTube facilitated groups. Consequently, a dominant qualitative approach is adopted using focus group discussion, interviews and tests to identify underlying patterns, methods and approaches to best fit academic writing guides and videos to improve user experiences of the media for academic writing. The fundamental results show that positive characterisations of user experiences include innovation, surprise, playfulness and stimulation whereas the narratives that are not satisfying are categorised as dissatisfaction, frustration, dissolution, disappointment, anger, confusion and irritation. Ultimately, the major findings of the research have the potential to improve user experiences on the platform by highlighting how and when the positive and negative experiences of users occur and a mapping of the differences in the academic writing performance between the two groups and best practices. Finally, the results have implications for pedagogy the fitting of YouTube videos to academic writing instruction.


Question: University students' views on utilizing Facebook as an interactive educational tool for learning.

This paper investigates the trends of information disclosure and privacy concerns within online social networks, specifically focusing on the popular college-oriented platform used by over 4,000 Carnegie Mellon University students. The study examines the extent to which these students share personal details and their engagement with the site's privacy controls. It is revealed that a significant amount of personal information is disclosed, with only a small fraction of users modifying the site's default, highly transparent privacy settings. The research underscores the risks to various facets of user privacy and the minimal effort made by the majority to protect their personal data, highlighting the need for greater awareness and proactive privacy management among social network users.

Over the past 15 years, educational technologists have been dabbling with a research technique known as quantitative content analysis (QCA). Although it is characterized as a systematic and objective procedure for describing communication, readers find insufficient evidence of either quality in published reports. In this paper, it is argued that QCA should be conceived of as a form of testing and measurement. If this argument is successful, it becomes possible to frame many of the problems associated with QCA studies under the well-articulated rubric of test validity. Two sets of procedures for developing the validity of a QCA coding protocol are provided, (a) one for developing a protocol that is theoretically valid and (b) one for establishing its validity empirically. The paper is concerned specifically with the use of QCA to study educational applications of computer-mediated communication. The primary role of networked computers in higher education has shifted from presenting structured, preprogrammed learning materials to facilitating communication. In turn, the role of educational technology researchers has expanded to include the role of communication researcher. In the late 1980s, studies began to appear that incorporated new perspectives, new methods, and new techniques. One of the most promising was quantitative content analysis (QCA). Berelson (1952) defined QCA as “a research technique for the systematic, objective, and quantitative description of the manifest content of communication” (p. 18). In this context, description is a process that includes segmenting communication content into units, assigning each unit to a category, and providing tallies for each category. Bullen (1998), for instance, studied participation and critical thinking in an online discussion by counting the number of times each student contributed to the discussion and by assigning each contribution to one of three categories of critical thinking. By 1999, enough of these types of studies had been conducted in the field of educational technology that we were able to prepare a literature review (Rourke, Anderson, Garrison, & Archer, 2001). We used published reports to illustrate many of the basic issues of QCA, including objectivity, reliability, units of analysis, types of content, research designs, and ethics. Our intent was to summarize the experiences of researchers like us who were struggling to apply this unfamiliar technique in a disciplined and efficient manner. We found the technique promising but chided researchers on the rigor of their reports, particularly on the lack of reliability data. Those who persisted with the technique found their own way to these conclusions, and today

BACKGROUND
During recent decades, self-regulated learning (SRL) has become a major research field. SRL successfully integrates the cognitive and motivational components of learning. Self-regulation is usually seen as an individual process, with the social aspects of regulation conceptualized as one aspect of the context. However, recent research has begun to investigate whether self-regulation processes are complemented by socially shared regulation processes.

AIMS
The presented study investigated what kind of socio-emotional challenges students experience during collaborative learning and whether the students regulate the emotions evoked during these situations. The interplay of the emotion regulation processes between the individual and the group was also studied.

SAMPLE
The sample for this study was 63 teacher education students who studied in groups of three to five during three collaborative learning tasks.

METHOD
Students' interpretations of experienced social challenges and their attempts to regulate emotions evoked by these challenges were collected following each task using the Adaptive Instrument for the Regulation of Emotions.

RESULTS
The results indicated that students experienced a variety of social challenges. Students also reported the use of shared regulation in addition to self-regulation. Finally, the results suggested that intrinsic group dynamics are derived from both individual and social elements of collaborative situations.

CONCLUSION
The findings of the study support the assumption that students can regulate emotions collaboratively as well as individually. The study contributes to our understanding of the social aspects of emotional regulation in collaborative learning contexts.

This study is aimed to investigate primary level students’ attitude and motivation and perceptions in learning English using Kahoot game. According to current development in Malaysian education field, 21st century learning style is more focused and given importance. Thus, Kahoot, game-based learning platform has been used to stimulate students’ from a rural area in southern Malaysia to learn English as their second language more effectively, actively and interestingly. The research design for this study is action research. Nine students were chosen as the target group for this study. The Kahoot game was conducted for three different topics in English after they completed the lesson each day. The data was collected using Attitude and Motivation Test Battery questionnaires consisting of 10 items, interview session with 5 semi-structured questions for three selected students and results of each Kahoot game played by the students. The data was analysed using descriptive analysis. The results of the questionnaires, interview session and results of the game were shown in figures and tables. Findings of the study show that all the nine students were able to engage actively in the game and they were able to master the target language effectively. They enjoy learning English using games and wish to have more games in future.

In this paper, we address the issue of personality and interaction style recognition from profile pictures in Facebook. We recruited volunteers among Facebook users and collected a dataset of profile pictures, labeled with gold standard self-assessed personality and interaction style labels. Then, we exploited a bag-of-visual-words technique to extract features from pictures. Finally, different machine learning approaches were used to test the effectiveness of these features in predicting personality and interaction style traits. Our good results show that this task is very promising, because profile pictures convey a lot of information about a user and are directly connected to impression formation and identity management.

The nonprofit phenomenon “TED,” the brand name for the concepts of Technology Education and Design, was born in 1984. It launched into pop culture stardom in 2006 when the organization’s curators began offering short, free, unrestricted, and educational video segments. Known as “TEDTalks,” these informational segments are designed to be no longer than 18 minutes in length and provide succinct, targeted enlightenment on various topics or ideas that are deemed “worth spreading.” TED Talks, often delivered in sophisticated studios with trendy backdrops, follow a format that focuses learners on the presenter and limited, extremely purposeful visual aids. Topics range from global warming to running to the developing world. Popular TED Talks, such as Sir Ken Robinson’s “Schools Kill Creatively” or Dan Gilbert’s “Why Are We Happy?” can easily garner well over a million views. TED Talks are a curious phenomenon for educators to observe. They are in many ways the antithesis of traditional lectures, which are typically 60-120 minutes in length and delivered in cavernous halls by faculty members engaged in everyday academic lives. Perhaps the formality of the lecture is the biggest superficial difference in comparison to casual TEDTalks (Table 1). However, TED Talks are not as unstructured as they may appear. Presenters are well coached and instructed to follow a specific presentation formula, whichmaximizes storyboarding and highlights passion for the subject. While learning is not formally assessed, TED Talks do seem to accomplish their goals of spreading ideas while sparking curiosity within the learner. The fact that some presentations have been viewed more than 16 million times points to the effectiveness of the platform in at least reaching learners and stimulating a desire to click, listen, and learn.Moreover, the TEDTalks website is the fourth most popular technology website and the single most popular conference and events website in the world. The TED phenomenon may have both direct and subliminal messages for academia. Perhaps an initial question to ponder is whether the TED phenomenon is a logical grassroots educational evolution or a reaction to the digital generation and their preference for learning that occurs “wherever, whenever.” The diverse cross-section of TED devotees ranging in background and age would seem to provide evidence that the platform does not solely appeal to younger generations of learners. Instead, it suggests that adult learners are either more drawn to digital learning than they think they are or than they are likely to admit. The perceived efficacy of TED once again calls into question the continued reliance of academia on the lecture as the primary currency of learning. TED Talks do not convey large chunks of information but rather present grander ideas. Would TED-like educational modules or blocks of 18-20 minutes be more likely to pique student curiosity across a variety of pharmacy topics, maintain attention span, and improve retention? Many faculty members who are recognized as outstanding teachers or lecturers might confess that they already teach through a TED-like lens. Collaterally, TED Talks or TED-formatted learning experiences might be ideal springboards for incorporation into inverted or flipped classroom environments where information is gathered and learned at home, while ideas are analyzed, debated, and assimilated within the classroom. Unarguably, TED Talks have given scientists and other researchers a real-time, mass media driven opportunity to disseminate their research, ideas, and theories that might otherwise have gone unnoticed. Similar platforms or approaches may be able to provide opportunities for the academy to further transmit research to the general public. The TED approach to idea dissemination is not without its critics. Several authors have criticized TED for flattening or dumbing down ideas so they fit into a preconceived, convenient format that is primarily designed to entertain. Consequently, the oversimplified ideas and conceptsmay provoke little effort from the learner to analyze data, theory, or controversy. Some Corresponding Author: Frank Romanelli, PharmD, MPH, 789 South Limestone Road, University of Kentucky College of Pharmacy, Lexington, KY 40536. E-mail: froma2@email. uky.edu American Journal of Pharmaceutical Education 2014; 78 (6) Article 113.

It is not—unless it triggers feelings of envy. This study uses the framework of social rank theory of depression and conceptualizes Facebook envy as a possible link between Facebook surveillance use and depression among college students. Using a survey of 736 college students, we found that the effect of surveillance use of Facebook on depression is mediated by Facebook envy. However, when Facebook envy is controlled for, Facebook use actually lessens depression. 2014 Elsevier Ltd. All rights reserved.

In this paper, an early intervention solution for collegiate faculty called Course Signals is discussed. Course Signals was developed to allow instructors the opportunity to employ the power of learner analytics to provide real-time feedback to a student. Course Signals relies not only on grades to predict students' performance, but also demographic characteristics, past academic history, and students' effort as measured by interaction with Blackboard Vista, Purdue's learning management system. The outcome is delivered to the students via a personalized email from the faculty member to each student, as well as a specific color on a stoplight -- traffic signal -- to indicate how each student is doing. The system itself is explained in detail, along with retention and performance outcomes realized since its implementation. In addition, faculty and student perceptions will be shared.

This article considers the developing field of learning analytics and argues that to move from small-scale practice to broad scale applicability, there is a need to establish a contextual framework that helps teachers interpret the information that analytics provides. The article presents learning design as a form of documentation of pedagogical intent that can provide the context for making sense of diverse sets of analytic data. We investigate one example of learning design to explore how broad categories of analytics—which we call checkpoint and process analytics—can inform the interpretation of outcomes from a learning design and facilitate pedagogical action.

Computer-based learning systems can now keep detailed logs of user-system interactions, including key clicks, eye-tracking, and video data, opening up new opportunities to study how students learn with technology. Educational Data Mining (EDM; Romero, Ventura, Pechenizkiy, & Baker, 2010) is concerned with developing, researching, and applying computerized methods to detect patterns in large collections of educational data – patterns that would otherwise be hard or impossible to analyze due to the enormous volume of data they exist within. Data of interest is not restricted to interactions of individual students with an educational system (e.g., navigation behavior, input to quizzes and interactive exercises) but might also include data from collaborating students (e.g., text chat), administrative data (e.g., school, school district, teacher), and demographic data (e.g., gender, age, school grades). Data on student affect (e.g., motivation, emotional states) has also been a focus, which can be inferred from physiological sensors (e.g., facial expression, seat posture and perspiration). EDM uses methods and tools from the broader field of Data Mining (Witten & Frank, 2005), a sub-field of Computer Science and Artificial Intelligence that has been used for purposes as diverse as credit card fraud detection, analysis of gene sequences in bioinformatics, or the analysis of purchasing behaviors of customers. Distinguishing EDM features are its particular focus on educational data and problems, both theoretical (e.g., investigating a learning hypothesis) and practical (e.g., improving a learning tool). Furthermore, EDM makes a methodological contribution by developing and researching data mining techniques for educational applications. Typical steps in an EDM project include data acquisition, data preprocessing (e.g., data “cleaning”), data mining, and validation of results.

This study extends the technology acceptance model to identify factors that influence technology acceptance among pre-service teachers in Ghana. Data from 380 usable questionnaires were tested against the research model. Utilising the extended technology acceptance model (TAM) as a research framework, the study found that: pre-service teachers’ pedagogical beliefs, perceived ease of use, perceived usefulness of computer technology and attitude towards computer use to be significant determinants of actual use of computer technology. Results obtained employing multiple stepwise regression analysis revealed that: (1) pre-service teachers’ pedagogical beliefs significantly influenced both perceived ease of use and perceived usefulness, (2) both perceived ease of use and perceived usefulness influence attitude towards computer use and attitude towards computer use significantly influences pre-service teachers’ actual use of computers. However, statistically, perceived ease of use did not significantly influence perceived usefulness. The findings contribute to the literature by validating the TAM in the Ghanaian context and provide several prominent implications for the research and practice of technology integration development.


Question: Students' views on utilizing Facebook as an interactive educational tool in higher education settings.

The Privacy Paradox in Social Networking: A Case Study of Teenagers and Students
In the digital age, teenagers and students willingly disclose personal information on online social networks, only to later express shock when this data is accessed by parents, educators, or the broader public. This phenomenon, often referred to as the privacy paradox, arises from the blurred boundaries between private and public spaces in the digital realm. Young individuals, particularly those in educational settings, often overlook the implications of their online activities, unaware that their digital footprint can be easily tracked by colleges and universities, affecting their academic and social lives. This article delves into the intricacies of the privacy paradox within social networking, examining the dichotomy between private and public spheres, and the specific privacy challenges faced by students. It further outlines potential strategies and recommendations aimed at addressing the privacy paradox, ensuring that students can engage in online social networks without compromising their personal data and privacy rights.

Interactive video in an e-learning system allows proactive and random access to video content. Our empirical study examined the influence of interactive video on learning outcome and learner satisfaction in e-learning environments. Four different settings were studied: three were e-learning environments—with interactive video, with non-interactive video, and without video. The fourth was the traditional classroom environment. Results of the experiment showed that the value of video for learning effectiveness was contingent upon the provision of interactivity. Students in the e-learning environment that provided interactive video achieved significantly better learning performance and a higher level of learner satisfaction than those in other settings. However, students who used the e-learning environment that provided non-interactive video did not improve either. The findings suggest that it may be important to integrate interactive instructional video into e-learning systems. # 2005 Elsevier B.V. All rights reserved.

AIMS
In 2011, online marketing became the largest marketing channel in the UK, overtaking television for the first time. This study aimed to describe the exposure of children and young adults to alcohol marketing on social media websites in the UK.

METHODS
We used commercially available data on the three most used social media websites among young people in the UK, from December 2010 to May 2011. We analysed by age (6-14 years; 15-24 years) and gender the reach (proportion of internet users who used the site in each month) and impressions (number of individual pages viewed on the site in each month) for Facebook, YouTube and Twitter. We further analysed case studies of five alcohol brands to assess the marketer-generated brand content available on Facebook, YouTube and Twitter in February and March 2012.

RESULTS
Facebook was the social media site with the highest reach, with an average monthly reach of 89% of males and 91% of females aged 15-24. YouTube had a similar average monthly reach while Twitter had a considerably lower usage in the age groups studied. All five of the alcohol brands studied maintained a Facebook page, Twitter page and YouTube channel, with varying levels of user engagement. Facebook pages could not be accessed by an under-18 user, but in most cases YouTube content and Twitter content could be accessed by those of all ages.

CONCLUSION
The rise in online marketing of alcohol and the high use of social media websites by young people suggests that this is an area requiring further monitoring and regulation.

Several studies have investigated a variety of factors affecting use of social networking sites (SNSs), but the investigation of these factors is still under development. In this study, we aim to contribute to the literature by extending and testing an existent conceptual model in a new context, including Life Satisfaction as an independent variable to explain the intention (We-Intention) to use Facebook. The previous models has Subjective Norm, Group Norms, Social Identity, Purposive Value, Self-Discovery, Maintaining Interpersonal Interconnectivity, Social Enhancement, Entertainment Value and Social Presence as the predictor variables. An online survey with Brazilians (n = 1111) was conducted. Our structural equation modeling reveals that Life Satisfaction influence on We-Intention is mediated by Subjective Norm, Group Norm, Social Identity, Entertainment Value, and Maintaining Interpersonal Interconnectivity (R-squared value is 0.36). Our findings, while consistent with existing literature in terms of theories, reveal different arrangements among factors influencing Brazilian consumers’ behavior. 2015 Elsevier Ltd. All rights reserved.

This paper explores opinion mining using supervised learning algorithms to find the polarity of the student feedback based on pre-defined features of teaching and learning. The study conducted involves the application of a combination of machine learning and natural language processing techniques on student feedback data gathered from module evaluation survey results of Middle East College, Oman. In addition to providing a step by step explanation of the process of implementation of opinion mining from student comments using the open source data analytics tool Rapid Miner, this paper also presents a comparative performance study of the algorithms like SVM, Naïve Bayes, K Nearest Neighbor and Neural Network classifier. The data set extracted from the survey is subjected to data preprocessing which is then used to train the algorithms for binomial classification. The trained models are also capable of predicting the polarity of the student comments based on extracted features like examination, teaching etc. The results are compared to find the better performance with respect to various evaluation criteria for the different algorithms.

The use of the web as a universal communication platform generates large volumes of data (Big data), which in many cases, need to be processed so that they can become useful knowledge in face of the sceptics who have doubts about the credibility of such information. The use of web data that comes from educational contexts needs to be addressed, since that large amount of unstructured information is not being valued, losing valuable information that can be used. To solve this problem, we propose the use of data mining techniques such as sentiment analysis to validate the information that comes from the educational platforms. The objective of this research is to propose a methodology that allows the user to apply sentiment analysis in a simple way, because although some researchers have done it, very few do with data in the educational context. The results obtained prove that the proposal can be used in similar cases.

We present the iterative design, prototype, and evaluation of CourseMIRROR (Mobile In-situ Reflections and Review with Optimized Rubrics), an intelligent mobile learning system that uses natural language processing (NLP) techniques to enhance instructor-student interactions in large classrooms. CourseMIRROR enables streamlined and scaffolded reflection prompts by: 1) reminding and collecting students' in-situ written reflections after each lecture; 2) continuously monitoring the quality of a student's reflection at composition time and generating helpful feedback to scaffold reflection writing; and 3) summarizing the reflections and presenting the most significant ones to both instructors and students. Through a combination of a 60-participant lab study and eight semester-long deployments involving 317 students, we found that the reflection and feedback cycle enabled by CourseMIRROR is beneficial to both instructors and students. Furthermore, the reflection quality feedback feature can encourage students to compose more specific and higher-quality reflections, and the algorithms in CourseMIRROR are both robust to cold start and scalable to STEM courses in diverse topics.

This paper presents learning analytics as a mean to improve students’ learning. Most learning analytics tools are developed by in-house individual educational institutions to meet the specific needs of their students. Learning analytics is defined as a way to measure, collect, analyse and report data about learners and their context, for the purpose of understanding and optimizing learning. The paper concludes by highlighting framework of learning analytics in order to improve personalised learning. In addition, it is an endeavour to define the characterising features that represents the relationship between learning analytics and personalised learning environment. The paper proposes that learning analytics is dependent on personalised approach for both educators and students. From a learning perspective, students can be supported with specific learning process and reflection visualisation that compares their respective performances to the overall performance of a course. Furthermore, the learners may be provided with personalised recommendations for suitable learning resources, learning paths, or peer students through recommending system. The paper’s contribution to knowledge is in considering personalised learning within the context framework of learning analytics.

This paper presents GISMO, a graphical interactive student monitoring and tracking system tool that extracts tracking data from an on-line course maintained with a Course Management System, and generates graphical representations that can be explored by course instructors to examine various aspects of distance students. GISMO uses techniques from Information Visualisation to render into an appropriate graphical manner the complex multidimensional student tracking data provided by the Course Management System. GISMO aims to help instructors to become aware of what is happening in their classes and provide a better support to their learners.

In this paper, we describe how people perceive the use of social media to support crime prevention in their communities. Based on survey and interview data from residents in high- and low-crime neighborhoods in Chicago, we found that African Americans, people from high-crime neighborhoods, and people with low levels of trust in local police are less likely to view social media as an appropriate tool to support citizens or the police in local crime prevention efforts. Residents' concerns include information getting into the wrong hands, trolls, and being perceived as a snitch. Despite concerns with usage, citizens also viewed social media as a tool that can supplement in-person crime prevention efforts and facilitate relationship-building and information sharing. We discuss the complexities of hyper-local usage of social media to combat crime by describing the social and historical contexts in which these tools exist.

Over the past 15 years, educational technologists have been dabbling with a research technique known as quantitative content analysis (QCA). Although it is characterized as a systematic and objective procedure for describing communication, readers find insufficient evidence of either quality in published reports. In this paper, it is argued that QCA should be conceived of as a form of testing and measurement. If this argument is successful, it becomes possible to frame many of the problems associated with QCA studies under the well-articulated rubric of test validity. Two sets of procedures for developing the validity of a QCA coding protocol are provided, (a) one for developing a protocol that is theoretically valid and (b) one for establishing its validity empirically. The paper is concerned specifically with the use of QCA to study educational applications of computer-mediated communication. The primary role of networked computers in higher education has shifted from presenting structured, preprogrammed learning materials to facilitating communication. In turn, the role of educational technology researchers has expanded to include the role of communication researcher. In the late 1980s, studies began to appear that incorporated new perspectives, new methods, and new techniques. One of the most promising was quantitative content analysis (QCA). Berelson (1952) defined QCA as “a research technique for the systematic, objective, and quantitative description of the manifest content of communication” (p. 18). In this context, description is a process that includes segmenting communication content into units, assigning each unit to a category, and providing tallies for each category. Bullen (1998), for instance, studied participation and critical thinking in an online discussion by counting the number of times each student contributed to the discussion and by assigning each contribution to one of three categories of critical thinking. By 1999, enough of these types of studies had been conducted in the field of educational technology that we were able to prepare a literature review (Rourke, Anderson, Garrison, & Archer, 2001). We used published reports to illustrate many of the basic issues of QCA, including objectivity, reliability, units of analysis, types of content, research designs, and ethics. Our intent was to summarize the experiences of researchers like us who were struggling to apply this unfamiliar technique in a disciplined and efficient manner. We found the technique promising but chided researchers on the rigor of their reports, particularly on the lack of reliability data. Those who persisted with the technique found their own way to these conclusions, and today


Question: Predicting students' grade point averages at graduation early: a data mining methodology.

In the realm of data mining, association rule mining was pioneered by Agrawal, Imielinski, and Swami in their seminal work [AIS93]. Building upon this foundation, Agrawal and Srikant introduced the Apriori algorithm for frequent itemset mining, a key technique in association rule discovery, as detailed in [AS94b]. Independently, Mannila, Tiovonen, and Verkamo devised a variant of the Apriori algorithm that employed a comparable pruning strategy [MTV94]. These pioneering efforts converged in a collaborative publication by Agrawal, Mannila, Srikant, Toivonen, and Verkamo [AMS96], which further refined the methodology for generating association rules from frequent itemsets, as outlined in [AS94a]. This body of work has been instrumental in shaping the concepts and techniques that underpin modern data mining practices.

**Question**: The Role of Social Media in Political Campaigns: A Case Study of the 2016 U.S. Presidential Election.

**Documents**: Social Media and the 2016 U.S. Presidential Campaign
The 2016 U.S. presidential campaign was dominated by social media, with candidates using platforms such as Facebook, Twitter, and Instagram to connect with voters. The campaign of Donald Trump was particularly effective in using social media to mobilize supporters and spread his message. Trump's use of Twitter to bypass traditional media and communicate directly with voters was a key strategy in his campaign. The Clinton campaign also used social media, but was less effective in mobilizing supporters and engaging with voters. The use of social media in the 2016 campaign had a significant impact on the outcome of the election, with many analysts arguing that Trump's social media strategy played a crucial role in his victory.

**Similar Documents**: In the 2016 U.S. Presidential Election, social media platforms like Facebook, Twitter, and Instagram emerged as central tools for political campaigns, enabling candidates to reach and engage with voters directly. Donald Trump's campaign was notably successful in leveraging social media, particularly Twitter, to galvanize supporters and disseminate his messages. By using Twitter to bypass traditional media channels, Trump was able to communicate his campaign narrative directly to the electorate, a tactic that proved highly effective. Hillary Clinton's campaign also incorporated social media, but it was less adept at rallying supporters and fostering voter engagement. The pivotal role of social media in the 2016 election is widely

This study focuses on the predictive identification of patients frequently revisiting the University of Virginia Health System Emergency Department. Identifying these patients can help the Emergency Department formulate strategies that improve patient care and decrease excess Emergency Department utilization. The Health System in particular faces a number of unique challenges in its ongoing mission to reduce extraneous patient revisits. In addition to its status as an academic hospital, it serves a broad geographic region as one of five level-I trauma centers in the Commonwealth of Virginia. In this study we utilized 5 years of data from the University of Virginia Health System data warehouse. These data contain information on 91,297 patients and 196,902 unique encounters, including details on patient demographics, diagnoses and hospital departments visited. From these raw data we engineered features, trained gradient boosted decision trees, and experimented with unsupervised clustering techniques to best approximate 30-day Emergency Department revisit risk at the conclusion of each patient encounter. Our best model for revisit risk resulted in a Receiver Operator Characteristic Area Under the Curve of 0.75. Furthermore, we exhibit the real-time performance of our model as a tool to rank which at-risk patients should receive priority for Emergency Department resources. This test demonstrated a significant improvement over the current allocation of Emergency Department social worker resources with a daily Mean Average Precision of 0.83. The methodologies proposed in this paper exhibit an end-to-end framework to transform raw administrative claims and limited clinical data into predictive models that help the Emergency Department better manage resources and target interventions.

Introduction Breast cancer (BC) is the most common cancer in women, affecting about 10% of all women at some stages of their life. In recent years, the incidence rate keeps increasing and data show that the survival rate is 88% after five years from diagnosis and 80% after 10 years from diagnosis [1]. Early prediction of breast cancer is one of the most crucial works in the follow-up process. Data mining methods can help to reduce the number of false positive and false negative decisions [2,3]. Consequently, new methods such as knowledge discovery in databases (KDD) has become a popular research tool for medical researchers who try to identify and exploit patterns and relationships among large number of variables, and predict the outcome of a disease using historical cases stored in datasets [4].

Online behavioural targeting is one of the most popular business strategies on the display advertising today. It is based primarily on analysing web user behavioural data with the usage of machine learning techniques with the aim to optimise web advertising. Being able to identify “unknown” and “first time seen” customers is of high importance in online advertising since a successful guess could identify “possible prospects” who would be more likely to purchase an advertisement’s product. By identifying prospective customers, online advertisers may be able to optimise campaign performance, maximise their revenue as well as deliver advertisements tailored to a variety of user interests. This work presents a hybrid approach benchmarking machine-learning algorithms and attribute preprocessing techniques in the context of behavioural targeting in process oriented environments. The performance of our suggested methodology is evaluated using the key performance metric in online advertising which is the predicted conversion rate. Our experimental results indicate that the presented process mining framework can significantly identify prospect customers in most cases. Our results seem promising, indicating that there is a need for further workflow research in online display

an introduction to data mining san jose state university introduction to data mining university of florida introduction to data mining exinfm introduction to data mining introduction to data mining and knowledge discovery lecture notes for chapter 3 introduction to data mining introduction to data mining introduction to data mining pennsylvania state university introduction to data mining tu/e introduction to data mining sas support session

One of the main problems faced by university students is to create and manage the semester course plan. In this paper, we present a course enrollment recommender system based on data mining techniques. The system mainly helps with students’ enrollment decisions. More specifically, it provides recommendation of selective and optional courses with respect to students’ skills, knowledge, interests and free time slots in their timetables. The system also warns students against difficult courses and reminds them mandatory study duties. We evaluate the usability of designed methods by analyzing real-world data obtained from the Information System of Masaryk University.

This paper presents a review of the literature on the application of data mining techniques for the detection of insurance fraud. Academic literature were analyzed and classified into three types of insurance fraud (automobile insurance, crop insurance and healthcare insurance) and six classes of data mining techniques (classification, regression, clustering, prediction, outlier detection, and visualization). The findings of this review clearly show that automobile insurance fraud detection have also attracted a great deal of attention in recent years. The main data mining techniques used for insurance fraud detection are logistic models, Decision tree, the Naïve Bayes, and support vector machine.

The pyramid method for content evaluation of automated summarizers produces scores that are shown to correlate well with manual scores used in educational assessment of students’ summaries. This motivates the development of a more accurate automated method to compute pyramid scores. Of three methods tested here, the one that performs best relies on latent semantics.

This paper describes an effort to model students' changing knowledge state during skill acquisition. Students in this research are learning to write short programs with the ACT Programming Tutor (APT). APT is constructed around a production rule cognitive model of programming knowledge, called theideal student model. This model allows the tutor to solve exercises along with the student and provide assistance as necessary. As the student works, the tutor also maintains an estimate of the probability that the student has learned each of the rules in the ideal model, in a process calledknowledge tracing. The tutor presents an individualized sequence of exercises to the student based on these probability estimates until the student has ‘mastered’ each rule. The programming tutor, cognitive model and learning and performance assumptions are described. A series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process. Currently the model is quite successful in predicting test performance. Further modifications in the modeling process are discussed that may improve performance levels.

Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing healthrelated data—the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients’ health conditions and responses to treatment over time. Methods: We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data. Results: For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the highest accuracy in predicting individual patient response ranged from 70% to 72% within the models tested. In terms of individual predictors, the Centerstone Assessment of Recovery Level—Adult (CARLA) baseline score was most significant in predicting outcome over time (odds ratio 4.1+2.27). Other variables with consistently significant impact on outcome included payer, diagnostic category, location and provision of case management services. Conclusions: This approach represents a promising avenue toward reducing the current gap between research and practice across healthcare, developing data-driven clinical decision support based on real-world populations, and serving as a component of embedded clinical artificial intelligences that ‘‘learn’’ over time. & 2012 Fellowship of Postgraduate Medicine. Published by Elsevier Ltd. All rights reserved. 2211-8837/$ see front matter & 2012 Fellowship of Postgraduate Medicine. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.hlpt.2012.03.001 Corresponding author at: Department of Informatics, Centerstone Research Institute, 365 South Park Ridge Road, Suite 103, Bloomington, IN 47401, United States. Tel.: +1 812 337 2302. E-mail addresses: Casey.Bennett@CenterstoneResearch.org (C.C. Bennett), Tom.Doub@CenterstoneResearch.org (T.W. Doub), RebeccaSelove@CenterstoneResearch.org (R. Selove). Health Policy and Technology (2012) 1, 105–114 Author's personal copy

Introduction. This study explored graduate students' information behaviour related to their process of inquiry and scholarly activities. Method. In depth, semi-structured interviews were conducted with one hundred graduate students representing all disciplines and departments from Carnegie Mellon University. Analysis. Working in pairs, we coded transcripts of interviews into meaningful categories using ATLAS.ti software. The combined use of quantitative and qualitative analysis aimed to reduce subjectivity. Results. Graduate students often begin with a meeting with professors who provide direction, recommend and provide resources. Other students help to shape graduate students' research activities, and university library personnel provide guidance in finding resources. The Internet plays a major role, although students continue to use print resources. Convenience, lack of sophistication in finding and using resources and course requirements affect their information behaviour. Findings vary across disciplines and between programmes. Conclusion. Libraries can influence students' information behaviour by re-evaluating their instructional programmes and provision of resources and services. They can take a lead by working with academic staff to guide students.