Question: Virtual : Reality : : Design : Interface

The Mindful Professional: Contemplating Action in Practice
Engaging with the mindful professional contemplating action in practice domain, what approach do you adopt to initiate your reading journey? Do you gravitate towards books that resonate with your interests or seek out titles that pique your curiosity? Every reader has their own motivation for delving into a book. Indeed, the habit of reading should ideally be cultivated from a young age. However, many individuals might not be drawn to reading books, but rather to other forms of literature. This is not a shortcoming. Some may find the prospect of opening a weighty tome filled with tiny print to be daunting. This is a common sentiment. Such feelings are not uncommon when faced with the prospect of reading the mindful professional contemplating action in practice. Yet, there is value in exploring texts that challenge us and broaden our perspectives, much like how the reflective practitioner: how professionals think in action invites us to consider the cognitive processes underlying professional practice. By delving into these works, we can gain insights into the nuanced ways in which professionals engage with their actions and decisions.

Virtual Reality (VR) sickness can cause intense discomfort, shorten the duration of a VR experience, and create an aversion to further use of VR. High-quality tracking systems can minimize the mismatch between a user's visual perception of the virtual environment (VE) and the response of their vestibular system, diminishing VR sickness for moving users. However, this does not help users who do not or cannot move physically the way they move virtually, because of preference or physical limitations such as a disability. It has been noted that decreasing field of view (FOV) tends to decrease VR sickness, though at the expense of sense of presence. To address this tradeoff, we explore the effect of dynamically, yet subtly, changing a physically stationary person's FOV in response to visually perceived motion as they virtually traverse a VE. We report the results of a two-session, multi-day study with 30 participants. Each participant was seated in a stationary chair, wearing a stereoscopic head-worn display, and used control and FOV-modifying conditions in the same VE. Our data suggests that by strategically and automatically manipulating FOV during a VR session, we can reduce the degree of VR sickness perceived by participants and help them adapt to VR, without decreasing their subjective level of presence, and minimizing their awareness of the intervention.

With the rapid development and wide spread of the virtual reality technology, we can easily find VR systems in various places such as school, library and home. Translation and rotation, the most frequently used gestures for manipulating objects in the real world, need to be implemented in order to make a user feel comfortable while manipulating 3D objects in the systems. In this paper, we propose a set of user-friendly 3D object manipulation gestures and develop a recognizer using Kinect for the VR systems. The usefulness and suitableness of the proposed system is shown from the user study performed.

Mobile Augmented Reality (MAR) blends the real world with digital objects especially in ubiquitous devices such as smartphones. The MAR applications provide an intelligent interface for users. In this, valuable digital information is advertised in physical spaces. However, the success of these applications is tied directly to the degree of user acceptance. This makes understanding the needs and expectations of the MAR’s potential users of paramount importance for designing and building the proper application. The objective of the paper is to expose an important gap in the development of novel applications in the virtual world. Previous research has shown that it is essential to study and understand the needs and expectations of the potential users of the upcoming application or system. Studying user needs and expectations before offering the developed application ensures a minimum level of acceptance and, of course, success. This paper presents a detailed study comprising of a userexperience (UX) evaluation of different prototypes through the use of three different UX evaluation methods. This kind of evaluation allows new developments to offer systems, which do not fail. The main contributions of this study are that it: 1) solicits expectations when consumers use MAR applications, 2) assesses the UX over different prototypes using three different metrics, 3) provides methodological insights on UX evaluation experiments and, 4) is useful for anyone who wants to develop handheld applications after understanding user expectations and how his experience should progress. The results of the study show that users value concreteness, realizability, personalization, novelty, intuitiveness and the usefulness of presented information. Paying attention to these factors can help develop more acceptable MAR applications and lead to more novel future designs.

Collaborative creativity is traditionally supported by formal techniques, such as brainstorming. These techniques improve the idea-generation process by creating group synergies, but also suffer from a number of negative effects. Current electronic tools to support collaborative creativity overcome some of these problems, but introduce new ones, by either losing the benefits of face-to-face communication or the immediacy of simultaneous contribution. Using an interactive environment as a test bed, we are investigating how collaborative creativity can be supported electronically while maintaining face-to-face communication. What are the design-factors influencing such a system? We have designed a brainstorming application that uses an interactive table and a large wall display, and compared the results of using it to traditional paper-based brainstorming in a user study with 30 participants. From the considerations that went into the design and the observations during the study we derive a number of design guidelines for collaborative systems in interactive environments.

In recentyears, electronicgames have assumed an importantplace in the lives of children and adolescents. Children acquire digital literacy informally, through plaj and neither schools nor other educational institutions take suficient account of this important aspect. We consider that multimedia design for training and education should combine the mostpowerfulfedtures of interactive multimedia design with the most effective principles of technologically-mediated learning. An examination of the evolution of the design of videogames is a good way to analyze the main contributions and characteristics ofgames-ba~edlearnin~ environments. At the same time, we will discuss the main obstacles and challenges to the use ofgames for learning. (

Several common systems satisfy some but not all of the VR definition above. Flight simulators provide vehicle tracking, not head tracking, and do not generally operate in binocular stereo. Omnimax theaters give a large angle of view [8], occasionally in stereo, but are not interactive. Head-tracked monitors [4][6] provide all but a large angle of view. Head-mounted displays (HMD) [7][13] and BOOMs [9] use motion of the actual display screens to achieve VR by our definition. Correct projection of the imagery on large screens can also create a VR experience, this being the subject of this paper. This paper describes the CAVE (CAVE Automatic Virtual Environment) virtual reality/scientific visualization system in detail and demonstrates that projection technology applied to virtual-reality goals achieves a system that matches the quality of workstation screens in terms of resolution, color, and flicker-free stereo. In addition, this format helps reduce the effect of common tracking and system latency errors. The off-axis perspective projection techniques we use are shown to be simple and straightforward. Our techniques for doing multi-screen stereo vision are enumerated, and design barriers, past and current, are described. Advantages and disadvantages of the projection paradigm are discussed, with an analysis of the effect of tracking noise and delay on the user. Successive refinement, a necessary tool for scientific visualization, is developed in the virtual reality context. The use of the CAVE as a one-to-many presentation device at SIGGRAPH '92 and Supercomputing '92 for computational science data is also mentioned. Previous work in the VR area dates back to Sutherland [12], who in 1965 wrote about the “Ultimate Display.” Later in the decade at the University of Utah, Jim Clark developed a system that allowed wireframe graphics VR to be seen through a headmounted, BOOM-type display for his dissertation. The common VR devices today are the HMD and the BOOM. Lipscomb [4] showed a monitor-based system in the IBM booth at SIGGRAPH '91 and Deering [6] demonstrated the Virtual Portal, a closets ized three-wal l project ion-based system, in the Sun Microsystems' booth at SIGGRAPH '92. The CAVE, our projection-based VR display [3], also premiered at SIGGRAPH '92. The Virtual Portal and CAVE have similar intent, but different implementation schemes.

This paper is an overview of technologies that fall under the term augmented reality. Augmented reality refers to a system in which the physical surroundings of a person are mixed with real-time computer generated information creating an enhanced perception of surrounding environment. Being partly virtual and real, augmented reality applications have quite extreme requirements to be practical to use. It also has very much potential in numerous different application areas. These issues make augmented reality both an interesting and challenging subject from scientific and business perspectives.

This paper presents a usage analysis and taxonomy of methods which are used to evaluate the usability of computer systems. To accommodate the analysis and taxonomy, a matrix of strategies which can be used for effective usability evaluation is presented. Such an analysis, taxonomy and strategies support human-computer interaction (HCI) professionals who have the responsibility for ensuring computer system usability. The strategies outlined are named Virtual Engineering, Soft Modelling, Hard Review and Real World. This paper also uses a composite set of existing popular generic evaluation methods which can be used as part of these strategies. The methods used are observation, questionnaire, interview, empirical methods, user groups, cognitive walkthroughs, heuristic methods, review methods and model methods. The paper continues by presenting a Usage Analysis Table of these methods and concludes by grouping them into a Taxonomy of Usability Evaluation Methods. A key emphasis of this paper is the appropriateness of individual methods to lifecycle timing.

Augmented reality (AR) technologies could enhance learning in several ways. The quality of an AR-based educational platform is a combination of key features that manifests in usability, usefulness, and enjoyment for the learner. In this paper, we present a multidimensional model to measure the quality of an AR-based application as perceived by students. The model is specified by a second-order factor (perceived quality) and three dimensions: ergonomic quality, learning quality, and hedonic quality. The purpose of this model is to embody previous research into a coherent framework for the evaluation of AR-based educational platforms and to provide guidance for researchers and practitioners. The model was empirically validated on a Chemistry learning scenario and the results confirm the importance of both the learning and hedonic quality. ARTICLE HISTORY Received 23 July 2014 Revised 2 December 2015 Accepted 9 December 2015

Done properly by experienced therapists, re-exposure to memories of traumatic events via imaginal exposure therapy can lead to a reduction of Post-traumatic Stress Disorder (PTSD) symptoms. Exposure helps the patient process and habituate to memories and strong emotions associated with the traumatic event: memories and emotions they have been carefully avoiding. But many patients are unwilling or unable to self-generate and re-experience painful emotional images. The present case study describes the treatment of a survivor of the World Trade Center (WTC) attack of 9-11-01 who had developed acute PTSD. After she failed to improve with traditional imaginal exposure therapy, we sought to increase emotional engagement and treatment success using virtual reality (VR) exposure therapy. Over the course of six 1-h VR exposure therapy sessions, we gradually and systematically exposed the PTSD patient to virtual planes flying over the World Trade Center, jets crashing into the World Trade Center with animated explosions and sound effects, virtual people jumping to their deaths from the burning buildings, towers collapsing, and dust clouds. VR graded exposure therapy was successful for reducing acute PTSD symptoms. Depression and PTSD symptoms as measured by the Beck Depression Inventory and the Clinician Administered PTSD Scale indicated a large (83%) reduction in depression, and large (90%) reduction in PTSD symptoms after completing VR exposure therapy. Although case reports are scientifically inconclusive by nature, these strong preliminary results suggest that VR exposure therapy is a promising new medium for treating acute PTSD. This study may be examined in more detail at www.vrpain.com.


Question: Virtual : Reality : : Design : Interface

This paper delves into the application of systems development as a research methodology within the realm of information systems (IS) research, presenting a comprehensive framework to elucidate its nature. The paper contrasts the use of this methodology across various engineering disciplines, with a particular focus on computer science and computer engineering. An integrated approach to IS research is proposed, encompassing theory building, systems development, experimentation, and observation. The authors review advancements in several application domains to substantiate the legitimacy of systems development as a research methodology. A detailed methodology for systems development research is presented, followed by an exploration of software engineering, the fundamental method for implementing this research methodology. The authors advocate for a complementary use of systems development alongside other research methodologies, emphasizing an integrated, multi-dimensional, and multi-methodological approach to generate impactful IS research outcomes. The central thesis is that systems development, experimentation, observation, and performance testing of developing systems can all contribute to research findings, and that a combination of these approaches is essential for a holistic investigation of research questions. This paper was initially published in the Proceedings of the Twenty-Third Hawaii International Conference on System Sciences (IEEE Computer Society Press, 1990) and was later featured in the Journal of Management Information Systems, Winter 1990-91, Vol. 7, No. 3, pp. 89-106. Copyright © M. E. Sharpe, Inc., 1991. 90 NUNAMAKER, CHEN, AND PURDIN.

Virtual reality, VR, offers many benefits to technical education, including the delivery of information through multiple active channels, the addressing of different learning styles, and experiential-based learning. This poster presents work performed by the authors to apply VR to engineering education, in three broad project areas: virtual chemical plants, virtual laboratory accidents, and a virtual UIC campus. The first area provides guided exploration of domains otherwise inaccessible, such as the interior of operating reactors and microscopic reaction mechanisms. The second promotes safety by demonstrating the consequences of not following proper lab safety procedures. And the third provides valuable guidance for (foreign) visitors. All programs developed are available on the Web, for free download to any interested parties.

This paper presents an application that uses hand gesture input to control a computer while giving a presentation. In order to develop a prototype of this application, we have defined an interaction model, a notation for gestures, and a set of guidelines to design gestural command sets. This works aims to define interaction styles that work in computerized reality environments. In our application, gestures are used for interacting with the computer as well as for communicating with other people or operating other devices.

In this paper we present an augmented reality (AR) application based on the QR Code. The system can extract the information embedded in a QR Code and show the information in an extended 3D form with the QR Code being the traditional AR marker. Traditional AR systems often use a particularly designed pattern (the marker) to recover the 3D scene structure and identify the object to be displayed on the scene. In these systems, the marker is used only for tracking and identification. They do not convey any other information. Consequently, the applications of these systems are limited and often a registration procedure is required for a new marker.
 QR Code has the advantage of large information capacity and is similar to an AR marker in appearance. Thus, more interesting and useful applications can be developed by combining the QR Code with the traditional AR system. In this paper, we combine these two techniques to develop a product demo system. A QR Code is pasted on the package of a product and then a 3D virtual object is displayed on the QR Code. This system allows the customer to visualize the product via a more direct and interactive way. Our system demonstrates the success of using QR Code as the AR marker to a particular application and we believe it can bring more convenient to our life in the future.

After prolonged stay in an intensive care unit (ICU) patients often complain about cognitive impairments that affect health-related quality of life after discharge. The aim of this proof-of-concept study was to test the feasibility and effects of controlled visual and acoustic stimulation in a virtual reality (VR) setup in the ICU. The VR setup consisted of a head-mounted display in combination with an eye tracker and sensors to assess vital signs. The stimulation consisted of videos featuring natural scenes and was tested in 37 healthy participants in the ICU. The VR stimulation led to a reduction of heart rate (p = 0. 049) and blood pressure (p = 0.044). Fixation/saccade ratio (p < 0.001) was increased when a visual target was presented superimposed on the videos (reduced search activity), reflecting enhanced visual processing. Overall, the VR stimulation had a relaxing effect as shown in vital markers of physical stress and participants explored less when attending the target. Our study indicates that VR stimulation in ICU settings is feasible and beneficial for critically ill patients.

Recently, there has been a substantial increase in efforts to develop 3-D visualization technologies that can provide the viewers with a realistic 3-D visual experience. Various terms such as “reality communication” have been used to categorize these efforts. In order to provide the viewers with a complete and realistic visual sensation, the display or visualization system and the displayed content need to match the physiological 3-D information sensing capabilities of the human visual system which can be quite complex. These may include spatial and temporal resolutions, depth perception, dynamic range, spectral contents, nonlinear effects, and vergence accommodation effects. In this paper, first we present an overview of some of the 3-D display research efforts which have been extensively pursued in Asia, Europe, and North America among other areas. Based on the limitations and comfort-based requirements of the human visual system when viewing a nonnatural visual input from 3-D displays, we present an analytical framework that combines main perception and human visual requirements with analytical tools and principles used in related disciplines such as optics, computer graphics, computational imaging, and signal processing. Building on the widely used notion of light fields, we define a notion of perceivable light fields to account for the human visual system physiological requirements, and propagate it back to the display device to determine the display device specifications. This helps us clarify the fundamental and practical requirements of the 3-D display devices for reality viewing communication. In view of the proposed analytical framework, we overview various methods that can be applied to overcome the extensive information needed to be displayed in order to meet the requirements imposed by the human visual system.

A s computers grow more powerful, less expensive , and more widely available, people are expecting them not only to perform obvious computational tasks, but also to assist in people-oriented tasks, such as writing , drawing, and designing. This shift is causing some user-interface (UI) researchers to rethink the traditional reliance on methods that are more machine-oriented and to look at ways to support properties like ambiguity, creativity, and informal communication. The idea is to bend computers to people's way of interacting , not the other way around. This flexibility is particularly important in the early stages of UI design itself, when designers need the freedom to sketch rough design ideas quickly, the ability to test designs by interacting with them, and the flexibility to fill in the design details as they make choices. 1 Tools at this stage must support conceptual design, which is characterized by ambiguity and the need to create several design variations quickly, as the " Why Sketching Is Important " sidebar describes. Unfortunately , with current UI tools, designers tend to focus on issues such as colors, fonts, and alignment, which are more appropriate later in the design. Thus, most UI designers resort to sketching ideas on paper, but these are hard to edit and inconvenient for user evaluations. (CMU) have designed, implemented, and evaluated SILK (Sketching Interfaces Like Krazy), an informal sketching tool that combines many of the benefits of paper-based sketching with the merits of current electronic tools. With SILK, designers can quickly sketch an interface using an electronic pad and stylus, and SILK recognizes widgets and other interface elements as the designer draws them. Unlike paper-based sketching, however, designers can exercise these elements in their sketchy state. For example, a sketched scrollbar is likely to contain an elevator or thumbnail, the small rectangle a user drags with a mouse. In a paper sketch, the elevator would just sit there, but in a SILK sketch, designers can drag it up and down, which lets them test the components' or widgets' behavior. SILK also supports the creation of storyboards—the arrangement of sketches to show how design elements behave, such as how a dialog box appears when the user activates a button. Storyboards are important because they give designers a way to show colleagues, customers, or end users early on how an interface will behave. Designers can test the interface at any point, not just …

There are currently no solutions for enabling direct face-to-face interaction between virtual reality (VR) users wearing head-mounted displays (HMDs). The main challenge is that the headset obstructs a significant portion of a user's face, preventing effective facial capture with traditional techniques. To advance virtual reality as a next-generation communication platform, we develop a novel HMD that enables 3D facial performance-driven animation in real-time. Our wearable system uses ultra-thin flexible electronic materials that are mounted on the foam liner of the headset to measure surface strain signals corresponding to upper face expressions. These strain signals are combined with a head-mounted RGB-D camera to enhance the tracking in the mouth region and to account for inaccurate HMD placement. To map the input signals to a 3D face model, we perform a single-instance offline training session for each person. For reusable and accurate online operation, we propose a short calibration step to readjust the Gaussian mixture distribution of the mapping before each use. The resulting animations are visually on par with cutting-edge depth sensor-driven facial performance capture systems and hence, are suitable for social interactions in virtual worlds.

Creating a design theory for Constructivist Gaming Learning Environment necessitates, among other things, the establishment of design principles. These principles have the potential to help designers produce games, where users achieve higher levels of learning. This paper focuses on twelve design principles: Probing, Distributed, Multiple Routes, Practice, Psychosocial Moratorium, Regime of Competence, Self-Knowledge, Collective Knowledge, Engaging, User Interface Ease of Use, On Demand and Just-in-Time Tutorial, and Achievement. We report on two pilot studies of a qualitative nature in which we test our design principles. Game play testing and observations were carried out on five Massively Multiplayer Online Games (MMOGs): RuneScape, GuildWars, Ragnarok, World of WarCraft, and Final Fantasy XI. Two educational games, Carabella Goes to College and Outbreak at WatersEdge were also observed. Our findings indicate that not all of the popular MMOGs and educational games support all of these principles.

In Augmented Reality, see-through HMDs superimpose virtual 3D objects on the real world. This technology has the potential to enhance a user's perception and interaction with the real world. However, many Augmented Reality applications will not be accepted until we can accurately register virtual objects with their real counterparts. In previous systems, such registration was achieved only from a limited range of viewpoints, when the user kept his head still. This paper offers improved registration in two areas. First, our system demonstrates accurate static registration across a wide variety of viewing angles and positions. An optoelectronic tracker provides the required range and accuracy. Three calibration steps determine the viewing parameters. Second, dynamic errors that occur when the user moves his head are reduced by predicting future head locations. Inertial sensors mounted on the HMD aid head-motion prediction. Accurate determination of prediction distances requires low-overhead operating systems and eliminating unpredictable sources of latency. On average, prediction with inertial sensors produces errors 2-3 times lower than prediction without inertial sensors and 5-10 times lower than using no prediction at all. Future steps that may further improve registration are outlined.

This chapter introduces the concept of human–computer interaction (HCI) design pattern—also called UI design pattern, interaction design patterns, HCI patterns, user experience pattern and usability engineering pattern. In this book, we mainly use the term HCI Design pattern, but we also use all these terms interchangeably to refer to HCI design pattern. HCI design patterns—have been introduced as a medium to capture and represent users’ experiences as well as to disseminate it as design knowledge. Patterns have the potential of transferring the design expertise of HCI designers to software engineers, who are usually unfamiliar with UI design and usability principles. What is the difference between design patterns and HCI design patterns? Why are they important among HCI designers and SE practitioners? Why design patterns have been considered as a HCI design tool? Why and how HCI design patterns can make a difference? This chapter provides a first answer to these questions that are the key objectives of this book. 1.1 Original Ideas About Design Pattern Among the early attempts to capture and use design knowledge in the format of design patterns, the first major milestone is often attributed to the architect Christopher Alexander, in the late 1970s. In his two books, A Pattern Language (Alexander 1977) and A Timeless Way of Building (Alexander 1979), Alexander, the father of patterns, discusses the avenues to capture and use design knowledge, and presents a large collection of pattern examples to help architects and engineers with the design of buildings, towns, and other urban entities. To illustrate the concept of pattern, Alexander proposes an architectural pattern called Wings of Light (Alexander 1977), where the problem is: Modern buildings are often shaped with no concern for natural light—they depend almost entirely on artificial light. But, buildings which displace natural light as the major source of illumination are not fit places to spend the day. Amongst other information such as design rationale, examples, and links to related patterns, the solution statement to this problem is: 1 The Patterns of HCI Design 2 Arrange each building so that it breaks down into wings which correspond, approximately, to the most important natural social groups within the building. Make each wing long and as narrow as you can—never more than 25 ft wide. According to Alexander, every pattern has three essential elements illustrated in Fig. 1.1, which are: a context, a problem, and a solution. The context describes a recurring set of situations in which the pattern can be applied. The problem refers to a set of forces, i.e., goals and constraints, which occur in the context. Generally, the problem describes when to apply the pattern. The solution refers to a design form or a design rule that can be applied to resolve the forces. It also describes the elements that constitute a pattern, relationships among these elements, as well as responsibilities and collaboration. All of Alexander’s patterns address recurrent problems that designers face by providing a possible solution within a specific context. They follow a similar structure, and the presented information is organized into pattern attributes, such as Problem and Design Rationale. Most noteworthy, the presented solution statement is abstract enough to capture only the invariant properties of good design. The specific pattern implementation is dependent on the design details and the designer’s creativity (Dix et al. 2003). In the example above, there is no mention of specific details such as the corresponding positions of wings to one another, or even the number of wings. These implementation details are left to the designer, allowing different instances of the same pattern solution. In addition, Alexander (1977) recognized that the design and construction of buildings required all stakeholders to make use of a common language for facilitating the implementation of the project from its very beginnings to its completion. If organized properly, patterns could achieve this for all the participants of a design project, acting as a communication tool for design. The pattern concept was not well known until 1987 when patterns appeared again at Object-Oriented Programming, Systems, Languages & Applications (OOPSLA), the object orientation conference in Orlando. There Kent Beck and Ward Cunningham (1987) introduced pattern languages for object-oriented program construction in a seminal paper. Since then many papers and presentations have appeared, authored by renowned software design practitioners such as Grady Booch, Richard Helm, Erich Gamma, and Kent Beck. In 1993, the formation of Hildside Group (1993) by Beck, Cunningham, Coplien, Booch, Johnson and others was the first step forward to forming a design patterns community in the field of software engineering.


Question: Detecting students at risk in massive open online courses.

This study introduces a predictive model for anticipating dropout rates among students enrolled in Massive Open Online Courses (MOOCs), utilizing machine learning techniques. Given the high attrition rates commonly seen in large-scale online courses, developing predictive mechanisms to identify at-risk students becomes crucial. While existing approaches have partially addressed this issue for students actively participating in online forums, there is a gap in addressing the broader student population. Our research focuses on analyzing click-stream data to develop a predictive model. The machine learning algorithm incorporates a comprehensive set of features, including a weekly history of student activities, enabling the detection of behavioral shifts over the course duration. As the course progresses and sufficient historical data becomes available, this approach demonstrates a significant improvement in predicting dropout rates compared to baseline methods. This predictive capability is particularly pronounced in the later stages of the course, when the algorithm can leverage a rich dataset of student interactions and engagement patterns.

The fields of learning theory and instructional design are in the midst of a scientific revolution in which their objectivist philosophical foundations are being replaced by a constructivist epistemology. This article describes the assumptions of a constructivist epistemology, contrasts them with objectivist assumptions, and then describes instructional systems that can support constructive learning at a distance Limitations of Distance Learning Technologies In an effort to supplement or replace live face-to-face instruction, technologicany mediated distance learning has more often than not merely replicated the ineffective methods that limit learning in face-to-face classrooms (Turoff 1995). Too often, potentially interactive technologies are used to present one-way lectures to students in remote locations. However, we believe that the most valuable activity in a classroom of any kind is the opportunity for students to work and interact together and to build and become part of a community of scholars and practitioners (Selfe and Eilola 1989; Bates 1990; Seaton 1993; Nalley 1995). A good learning experience is one in which a student can "master new knowledge and skills, criticany examine assumptions and beliefs, and engage in an invigorating, collaborative quest for wisdom and personal, holistic development" (Eastmond and Ziegahn 1995, 59). Technology used in distance education should facilitate these "good learning experiences" in an "extended classroom model" rather than broadcast teacher-centered lectures and demonstrations (Burge and Roberts 1993). A significant impediment to this goal is the fact that many teachers and instructional designers come to distance education from traditional backgrounds, bringing with them assumptions about teaching and learning that are not theory-based and do not translate well to technologicany mediated instruction (Schieman, Taere, and McLaren 1992).

This study aimed to create an online community of practice by creating a virtual classroom in the Edmodo application and ascertain the opinions of pre-service primary teachers about the effects of Edmodo on their learning to teach science and availability of Edmodo. The research used a case study, which is one method of descriptive research. During the implementation process, pre-service primary teachers used Edmodo to share activities they had designed that centred on the scientific concepts taught in primary science education programmes. They also shared their diary entries that outlined their experiences and views after they had practised their activities in a real classroom. 58 pre-service primary teachers participated in the study. The author developed a questionnaire and it included one closed-ended and six open-ended questions; the questionnaire was used as the data collection tool. The pre-service primary teachers used Edmodo for 12 weeks. Descriptive and content analysis methods were used to analyse the data obtained from the study. The results obtained from the data analysis showed that pre-service primary teachers generally had positive views about the use of Edmodo in teacher education programmes. Most pre-service primary teachers stated that Edmodo provides the possibility of sharing knowledge, experiences and views. However, some pre-service teachers stated that Edmodo has some limitations; for example, the fact that it requires the user to have internet access. As a result, it can be said that Edmodo can be used to create an online community of practice in teacher education programmes.

Massive Online Open Courses (MOOCs) which were introduced in 2008 has since drawn attention around the world for both its advantages as well as criticism on its drawbacks. One of the issues in MOOCs which is the lack of interactivity with the instructor has brought conversational bot into the picture to fill in this gap. In this study, a prototype of MOOCs conversational bot, MOOC-bot is being developed and integrated into MOOCs website to respond to the learner’s enquiries using text or speech input. MOOC-bot is using the popular Artificial Intelligence Markup Language (AIML) to develop its knowledge base, leverage from AIML’s capability to deliver appropriate responses and can be quickly adapted to new knowledge domains. The system architecture of MOOC-bot consists of knowledge base along with AIML interpreter, chat interface, MOOCs website and Web Speech API to provide speech recognition and speech synthesis capability. The initial MOOC-bot prototype has the general knowledge from the past Loebner Prize winner ALICE, course’s frequent asked questions, and a course’s content offered by Universiti Teknikal Malaysia Melaka (UTeM). The evaluation of MOOC-bot based on the past competition questions from Chatterbox Challenge (CBC) and Loebner Prize has shown that it was able to provide correct answers most of the time during the test and demonstrated the capability to prolong the conversation. The advantages of MOOCbot such as able to provide 24-hour service that can serve different time zones, able to have knowledge in multiple domains, and can be shared by multiple sites simultaneously have outweighed its existing limitations.

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

Today, as social media sharing increases, the instant emotional state of the students changes frequently. This situation is greatly affecting the learning process as well as the motivation of the students in the classroom. Sometimes, it is insufficient for the educator to observe the emotional states of students. Therefore, an automatic system is needed that can detect and analyze the emotional states of students in the classroom. In this study, an auxiliary information system, which uses image processing and human-computer interaction, has been developed that would be used in the field of education. In this system, the dataset obtained from the students' faces was tested using various machine learning algorithms. As a result, the accuracy of this system was found as 97.15% using support vector machine. This system is aimed to direct the educator to communicate with the students and to increase their motivation when necessary.

In this research paper we consider formative assessment (FA) and discuss ways in which it has been implemented in four different university courses. We illustrate the different aspects of FA by deconstructing it and then demonstrating effectiveness in improving both teaching and student achievement. It appears that specifically “what is done” was less important since there were positive achievement gains in each study. While positive gains were realized with use of technology, gains were also realized with implementation of nontechnology dependent techniques. Further, gains were independent of class size or subject matter.

During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection is in the hands of the users. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. With the aim of fostering their awareness on private data leakage risk, some measures have been proposed that quantify the privacy risk of each user. However, these measures do not capture the objective risk of users since they assume that all user’s direct social connections are close (thus trustworthy) friends. Since this assumption is too strong, in this paper we propose an alternative approach: each user decides which friends are allowed to see each profile item/post and our privacy score is defined accordingly. We show that it can be easily computed with minimal user intervention by leveraging an active learning approach. Finally, we validate our measure on a set of real Facebook users.

There are rising rates of depression on college campuses. Mental health services on our campuses are working at full stretch. In response researchers have proposed using mobile sensing for continuous mental health assessment. Existing work on understanding the relationship between mobile sensing and depression, however, focuses on generic behavioral features that do not map to major depressive disorder symptoms defined in the standard mental disorders diagnostic manual (DSM-5). We propose a new approach to predicting depression using passive sensing data from students' smartphones and wearables. We propose a set of symptom features that proxy the DSM-5 defined depression symptoms specifically designed for college students. We present results from a study of 83 undergraduate students at Dartmouth College across two 9-week terms during the winter and spring terms in 2016. We identify a number of important new associations between symptom features and student self reported PHQ-8 and PHQ-4 depression scores. The study captures depression dynamics of the students at the beginning and end of term using a pre-post PHQ-8 and week by week changes using a weekly administered PHQ-4. Importantly, we show that symptom features derived from phone and wearable sensors can predict whether or not a student is depressed on a week by week basis with 81.5% recall and 69.1% precision.

Student-generated concept maps (Cmaps) have been the preferred choice to design assessment tasks, which are time-consuming in real classroom settings. We have proposed the use of Cmap with errors (elaborated by teachers) to develop assessment tasks, as a way to address the logistical practicality obstacles usually found in classrooms. This paper compared two different tasks, finding the errors and judging the selected propositions in Cmap with errors, exploring two topics with different levels of difficulty. Our results confirmed Cmap with errors as a straightforward approach to include concept mapping into the classroom routine to foster the pedagogic resonance (the bridge between teacher knowledge and student learning), which is critical for motivating students to learn meaningfully. Moreover, Cmaps with errors are also amenable for on-line learning platforms. Future works of our research group will develop an automated process for evaluation and feedback using the task formats presented in this paper.

The importance of accurate estimation of student’s future performance is essential in order to provide the student with adequate assistance in the learning process. To this end, this research aimed at investigating the use of Bayesian networks for predicting performance of a student, based on values of some identified attributes. We presented empirical experiments on the prediction of performance with a data set of high school students containing 8 attributes. The paper demonstrates an application of the Bayesian approach in the field of education and shows that the Bayesian network classifier has a potential to be used as a tool for prediction of student performance.


Question: Detecting students at risk in massive open online courses.

This paper explores the calibration of probability predictions from various supervised learning algorithms, particularly in relation to true posterior probabilities. We uncover distinct patterns in the predictions made by different models. Maximum margin methods, including boosted trees and boosted stumps, exhibit a tendency to push probability estimates away from the extremes (0 and 1), resulting in a characteristic sigmoid-shaped distortion. Conversely, models like Naive Bayes, which assume unrealistic independence among features, tend to push probabilities towards the extremes. Neural networks and bagged trees, on the other hand, do not exhibit these biases and produce well-calibrated probabilities. To address the biases in probability predictions, we investigate two calibration methods: Platt Scaling and Isotonic Regression. We analyze the types of distortions these methods can correct and evaluate the amount of data required for their effectiveness. Empirical results indicate that, after calibration, boosted trees, random forests, and support vector machines (SVMs) yield the most accurate probability predictions.

**Question**: The Role of Social Media in Political Campaigns

**Documents**: The Impact of Social Media on Political Campaigns in the United States
Social media platforms have fundamentally altered the landscape of political campaigns in the United States. This paper examines the influence of social media on the strategies and tactics employed by political campaigns. We analyze data from recent elections to understand how social media has been integrated into campaign strategies, including its role in fundraising, voter mobilization, and messaging. We find that social media has become a critical tool for candidates to connect directly with voters, bypassing traditional media gatekeepers. The paper also explores the challenges posed by social media, such as the spread of misinformation and the potential for echo chambers. The analysis reveals that successful campaigns are those that effectively leverage social media to engage a broad audience, tailor messages to specific demographics, and mobilize supporters.

**Similar Documents**: The Evolution of Political Campaigns in the Digital Age: A Focus on Social Media
In recent years, social media has emerged as a pivotal element in the evolution of political campaigns, particularly in the United States. This study investigates the transformative impact of social media on campaign methodologies, strategies, and outcomes. By scrutinizing data from contemporary elections, we highlight how social media has been seamlessly incorporated into campaign blueprints, serving as a potent channel for fundraising, voter engagement, and tailored messaging. We observe that social media enables candidates to establish direct lines of communication with voters, circumventing traditional media filters. However, the study also acknowledges the downsides of social media, including the proliferation of false information and the

0747-5632/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.chb.2012.08.001 ⇑ Corresponding author. Tel.: +886 02 7734 3347; f E-mail address: joum@ntnu.edu.tw (M. Jou). Cloud computing technology has matured as it has been integrated with every kind of digitalization processes. It offers numerous advantages for data and software sharing, and thus making the management of complex IT systems much simpler. For education in engineering, cloud computing even provides students with versatile and ubiquitous access to software commonly used in the field without having to step into an actual computer lab. Our study analyzed learning attitudes and academic performances induced by the utilization of resources driven by cloud computing technologies. Comparisons were made between college students with high school and vocational high school backgrounds. One hundred and thirtytwo students who took the computer-aided designing (CAD) course participated in the study. Technology Acceptance Model (TAM) was used as the fundamental framework. Open-ended sets of questionnaires were designed to measure academic performance and causal attributions; the results indicated no significant differences in the cognitive domain between the two groups of students, though it is not so in both the psychomotor and the affective domains. College students with vocational high school background appeared to possess higher learning motivation in CAD applications. 2012 Elsevier Ltd. All rights reserved.

This paper is investigating if it is possible to predict source code quality based on static analysis and machine learning. The proposed approach includes a plugin in Eclipse, uses a combination of peer review/human rating, static code analysis, and classification methods. As training data, public data and student hand-ins in programming are used. Based on this training data, new and uninspected source code can be accurately classified as “well written” or “badly written”. This is a step towards feedback in an interactive environment without peer assessment.

We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media. We exploit a unique dataset containing millions of tweets generated by more than 25 thousand users who have been manually identified, reported, and suspended by Twitter due to their involvement with extremist campaigns. We also leverage millions of tweets generated by a random sample of 25 thousand regular users who were exposed to, or consumed, extremist content. We carry out three forecasting tasks, (i) to detect extremist users, (ii) to estimate whether regular users will adopt extremist content, and finally (iii) to predict whether users will reciprocate contacts initiated by extremists. All forecasting tasks are set up in two scenarios: a post hoc (time independent) prediction task on aggregated data, and a simulated real-time prediction task. The performance of our framework is extremely promising, yielding in the different forecasting scenarios up to 93% AUC for extremist user detection, up to 80% AUC for content adoption prediction, and finally up to 72% AUC for interaction reciprocity forecasting. We conclude by providing a thorough feature analysis that helps determine which are the emerging signals that provide predictive power in different scenarios.

Although there is evidence that academically successful students are engaged with their studies, it has proved difficult to define student engagement clearly. Student engagement is commonly construed as having two dimensions, social and academic. The rapid adoption of social media and digital technologies has ensured increasing interest in using them for improving student engagement. This paper examines Facebook usage among a first year psychology student cohort and reports that although the majority of students (94%) had Facebook accounts and spent an average of one hour per day on Facebook, usage was found to be predominantly social. Personality factors influenced usage patterns, with more conscientious students tending to use Facebook less than less conscientious students. This paper argues that, rather than promoting social engagement in a way that might increase academic engagement, it appears that Facebook is more likely to operate as a distracting influence.

Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.

This article examines work related to the development and validation of a measurement tool for the Community of Inquiry (CoI) framework in online settings. The framework consists of three elements: social presence, teaching presence and cognitive presence, each of which is integral to the instrument. The 34 item instrument, and thus framework, was tested after being administered at four institutions in the Summer of 2007. The article also includes a discussion of implications for the future use of the CoI survey and the CoI framework itself.

Researchers have highlighted how tracking learners’ eye-gaze can reveal their reading behaviors and strategies, and this provides a framework for developing personalized feedback to improve learning and problem solving skills. In this paper, we describe analyses of eye-gaze data collected from 16 middle school students who worked with Betty’s Brain, an open-ended learning environment, where students learn science by building causal models to teach a virtual agent. Our goal was to test whether newly available consumer-level eye trackers could provide the data that would allow us to probe further into the relations between students’ reading of hypertext resources and building of graphical causal maps. We collected substantial amounts of gaze data and then constructed classifier models to predict whether students would be successful in constructing correct causal links. These models predicted correct map-building actions with an accuracy of 80% (F1 = 0.82; Cohen’s kappa κ = 0.62). The proportions of correct link additions are in turn directly related to learners’ performance in Betty's Brain. Therefore, students’ gaze patterns when reading the resources may be good indicators of their overall performance. These findings can be used to support the development of a real-time eye gaze analysis system, which can detect students reading patterns, and when necessary provide support to help them become better readers.

Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning process. In this study we have studied various tasks and applications existing in the field of EDM and categorized them based on their purposes. We have compared our study with other existing surveys about EDM and reported a taxonomy of task.

The ability to predict the students’ academic performance is very important in institution educational system. Recently some researchers have been proposed data mining techniques for higher education. In this paper, we compare two data mining techniques which are: Artificial Neural Network (ANN) and the combination of clustering and decision tree classification techniques for predicting and classifying students’ academic performance. The data set used in this research is the student data of Computer Science Department, Faculty of Science and Defence Technology, National Defence University of Malaysia (NDUM).

This paper reports on the implication of different preferred learning styles on students' performance in the introductory programming sequence and on work in progress on how to accommodate these different styles.Students were given a learning styles preference test and then their preferred learning styles were compared to their performance on the exam and the practical programming part of the introductory programming module. There were significant differences in performance between groups of students.This result could lead one to two possible conclusions. One might be that some students' learning styles are more suited to learning programming than others.An alternative explanation is that our current methods of teaching advantage students with certain learning preference styles. We are at present in the process of testing this second assumption by providing students with a wider range of learning materials. We will then see if student performance is improved by using our current results as a baseline for comparison


Question: Detecting students at risk in massive open online courses.

This paper explores the application of Hidden Markov Models (HMMs) for forecasting student attrition in Massive Open Online Courses (MOOCs), an area characterized by notably high dropout rates. By analyzing students' historical activity patterns within a course, predictions can be made regarding the likelihood of a student discontinuing their participation in the subsequent week. This capability is instrumental in designing timely interventions to enhance student retention. The study benchmarks the predictive power of various modeling methodologies and feature sets, which are derived from distinct student behaviors. The most effective prediction model employs an HMM to track sequences of student actions longitudinally, consolidating multiple continuous features into a unified discrete observable state through a straightforward cross-product technique. This approach delivered a Receiver Operating Characteristic Area Under the Curve (ROC AUC) score of 0.710, significantly outperforming a random prediction model. Additionally, the paper utilizes rudimentary HMMs to elucidate the student behaviors that are most indicative of retention.

This research was supported with a grant from Microsoft MIT iCampus as a part of the Games-to-Teach Project. The authors would like to thank the PIs of the Games-to-Teach Project, Randy Hinrichs at Microsoft Research and Henry Jenkins, Director MIT Comparative Media Studies for their support of this project, as well as Kodjo Hesse, Gunnar Harboe, and Walter Holland for their hard work in the development of Environmental Detectives.

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

The goal of this paper is to address the question: ‘why do lurkers lurk?’ Lurkers reportedly makeup the majority of members in online groups, yet little is known about them. Without insight into lurkers, our understanding of online groups is incomplete. Ignoring, dismissing, or misunderstanding lurking distorts knowledge of life online and may lead to inappropriate design of online environments. To investigate lurking, the authors carried out a study of lurking using in-depth, semi-structured interviews with ten members of online groups. 79 reasons for lurking and seven lurkers’ needs are identified from the interview transcripts. The analysis reveals that lurking is a strategic activity involving more than just reading posts. Reasons for lurking are categorized and a gratification model is proposed to explain lurker behavior.

Sebastian Thrun1, Mike Montemerlo1, Hendrik Dahlkamp1, David Stavens1, Andrei Aron1, James Diebel1, Philip Fong1, John Gale1, Morgan Halpenny1, Gabriel Hoffmann1, Kenny Lau1, Celia Oakley1, Mark Palatucci1, Vaughan Pratt1, Pascal Stang1, Sven Strohband2, Cedric Dupont2, Lars-Erik Jendrossek2, Christian Koelen2, Charles Markey2, Carlo Rummel2, Joe van Niekerk2, Eric Jensen2, Philippe Alessandrini2, Gary Bradski3, Bob Davies3, Scott Ettinger3, Adrian Kaehler3, Ara Nefian3, and Pamela Mahoney4

This paper presents a data mining methodology to analyze the careers of University graduated students. We present different approaches based on clustering and sequential patterns techniques in order to identify strategies for improving the performance of students and the scheduling of exams. We introduce an ideal career as the career of an ideal student which has taken each examination just after the end of the corresponding course, without delays. We then compare the career of a generic student with the ideal one by using the different techniques just introduced. Finally, we apply the methodology to a real case study and interpret the results which underline that the more students follow the order given by the ideal career the more they get good performance in terms of graduation time and final grade. 2015 Elsevier Ltd. All rights reserved.

The
  education
  of
  the
  poor
  and
  disadvantaged
  population
  has
  been
  a
  long-‐standing challenge
  to
  the
  education
  system
  in
  both
  developed
  and
  developing
  countries. Although
  computer-‐assisted
  learning
  (CAL)
  has
  been
  considered
  one
  alternative
  to improve
  learning
  outcomes
  in
  a
  cost-‐effective
  way,
  the
  empirical
  evidence
  of
  its
  impacts on
  improving
  learning
  outcomes
  is
  mixed.
  This
  paper
  intends
  to
  explore
  the
  nature
  of the
  effects
  of
  CAL
  on
  student
  academic
  and
  non-‐academic
  outcomes
  for
  underserved populations
  in
  a
  developing
  country.
  To
  meet
  this
  goal,
  we
  exploit
  a
  randomized
  field experiment
  of
  a
  CAL
  program
  involving
  over
  4000
  third-‐grade
  students,
  mostly
  from poor
  migrant
  families,
  in
  43
  migrant
  schools
  in
  Beijing.
  The
  main
  intervention
  is
  a
  math CAL
  program
  that
  is
  held
  out
  of
  regular
  school
  hours.
  The
  program
  is
  tailored
  to
  the regular
  school
  math
  curriculum
  and
  is
  remedial
  in
  nature.
  Our
  results
  show
  that,
  the
  CAL program
  improved
  the
  student
  standardized
  math
  scores
  by
  0.14
  standard
  deviations and
  most
  of
  the
  program
  effect
  took
  place
  within
  two
  months
  after
  the
  start
  of
  the program.
  Low-‐performing
  students
  and
  those
  with
  less-‐educated
  parents
  benefited more
  from
  the
  program.
  Moreover,
  CAL
  also
  significantly
  increased
  the
  levels
  of
  self-‐ efficacy
  of
  the
  students
  and
  their
  interest
  in
  learning.
  We
  observed
  at
  most
  a
  moderate program
  spillover
  in
  Chinese
  test
  scores.
  Our
  findings
  are
  robust
  to
  the
  Hawthorne effect
  and
  CAL
  program
  spillovers
  that
  might
  potentially
  bias
  the
  estimates
  of
  the program
  effects. Key
  Words:

  Education;
  Development;
  Computer
  Assisted
  Learning;
  Random Assignment;
  Test
  Scores;
  China;
  Migration JEL
  Codes:
  I20;
  I21;
  I28;
  O15

This paper compares the accuracy of decision tree and Bayesian network algorithms for predicting the academic performance of undergraduate and postgraduate students at two very different academic institutes: Can Tho University (CTU), a large national university in Viet Nam; and the Asian Institute of Technology (AIT), a small international postgraduate institute in Thailand that draws students from 86 different countries. Although the diversity of these two student populations is very different, the data-mining tools were able to achieve similar levels of accuracy for predicting student performance: 73/71% for {fail, fair, good, very good} and 94/93% for {fail, pass} at the CTU/AIT respectively. These predictions are most useful for identifying and assisting failing students at CTU (64% accurate), and for selecting very good students for scholarships at the AIT (82% accurate). In this analysis, the decision tree was consistently 3-12% more accurate than the Bayesian network. The results of these case studies give insight into techniques for accurately predicting student performance, compare the accuracy of data mining algorithms, and demonstrate the maturity of open source tools.

Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.

Information security awareness web-based courses are commonly recommended in cyber security strategies to help build a security culture capable of addressing information systems breaches caused by user mistakes whose negligence or ignorance of policies may endanger information systems assets. A research gap exists on the impact of Information Security Awareness Web-Based Courses: these are failing in changing to a significant degree the behavior of participants regarding compliance and diligence, which translates into continuous vulnerabilities. The aim of this work is to contribute with a theoretical and empirical analysis on the potential strengths and weaknesses of Information Security Awareness Web-Based Courses and with two practical tools readily applicable for designers and reviewers of web-based or mediatized courses on information security awareness and education. The research design seeks to respond two research questions. The first on the formulation of a minimum set of criteria that could be applied to Information Security Awareness Web-Based Courses, to support their real impact on employee’s diligence and compliance, resulting in eleven criteria for courses’ assessment and a checklist. The second, about a controlled experiment to explore the actual impact of an existing course, in respect to diligence and compliance using phishing emails as educational tools, that reaffirms the theoretical assumptions arrived to earlier. The development of minimum criteria and their systematic implementation pursue behavioral change, emphasizes the importance of disciplinary integration in cyber security research, and advocates for the development of a solid security culture of diligence and compliance, capable of supporting the protection of organizations from information system threats. The results gathered in this study suggest that achieving positive results in the existing information security tests that follow security awareness courses does not necessarily imply that diligence or information security policies compliance are affected. These preliminary findings accumulate evidence on the importance of implementing the recommendations formulated in this work.

In this paper, we propose the attention extraction method on a textbook of physics by using eye tracking glasses. We prepare a document including text and tasks in physics, and record reading behavior on the document from 6-grade students. The result confirms that students pay attention to a different region depending on situations (using the text as a material to learn the content, or using it as hints to solve tasks) and their comprehension.


Question: Detecting students at risk in massive open online courses.

This paper explores the engagement patterns of students in Massive Open Online Courses (MOOCs), leveraging the extensive digital traces left by learners. Given the web-based nature of MOOCs, every student's interaction with course materials is meticulously recorded, offering a unique opportunity to analyze learner behavior with an unparalleled level of detail and scale. Our study aims to construct a comprehensive framework for understanding how individuals interact with MOOCs.
 We introduce a classification system to categorize individual learner behavior and scrutinize the engagement patterns of students who achieve high and low grades. Additionally, we delve into the relationship between forum participation and other course activities. As part of our investigation, we conducted a large-scale badge deployment in a MOOC, incorporating randomized experiments to test the impact of varying badge visibility among different student subgroups. Our findings indicate that increasing the prominence of badges led to a notable rise in forum activity, highlighting the potential of badges as motivational tools to enhance engagement in MOOCs. This research provides valuable insights into student behavior in online learning environments and underscores the importance of gamification elements in fostering active participation.

Today, as social media sharing increases, the instant emotional state of the students changes frequently. This situation is greatly affecting the learning process as well as the motivation of the students in the classroom. Sometimes, it is insufficient for the educator to observe the emotional states of students. Therefore, an automatic system is needed that can detect and analyze the emotional states of students in the classroom. In this study, an auxiliary information system, which uses image processing and human-computer interaction, has been developed that would be used in the field of education. In this system, the dataset obtained from the students' faces was tested using various machine learning algorithms. As a result, the accuracy of this system was found as 97.15% using support vector machine. This system is aimed to direct the educator to communicate with the students and to increase their motivation when necessary.

This qualitative study explored the feelings of students about using the library for research. Personal writing, collected in beginning composition courses over a two-year period, was analyzed for recurrent themes. It was found that 75 to 85 percent of the students in these courses described their initial response to library research in terms of fear. Three concepts emerged from these descriptions: (1) students generally feel that their own library-use skills are inadequate while the skills of other students are adequate, (2) the inadequacy is shameful and should be hidden, and (3) the inadequacy would be revealed by asking questions. A grounded theory of library anxiety was constructed from these data.

This paper compares the accuracy of decision tree and Bayesian network algorithms for predicting the academic performance of undergraduate and postgraduate students at two very different academic institutes: Can Tho University (CTU), a large national university in Viet Nam; and the Asian Institute of Technology (AIT), a small international postgraduate institute in Thailand that draws students from 86 different countries. Although the diversity of these two student populations is very different, the data-mining tools were able to achieve similar levels of accuracy for predicting student performance: 73/71% for {fail, fair, good, very good} and 94/93% for {fail, pass} at the CTU/AIT respectively. These predictions are most useful for identifying and assisting failing students at CTU (64% accurate), and for selecting very good students for scholarships at the AIT (82% accurate). In this analysis, the decision tree was consistently 3-12% more accurate than the Bayesian network. The results of these case studies give insight into techniques for accurately predicting student performance, compare the accuracy of data mining algorithms, and demonstrate the maturity of open source tools.

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

In this paper we explore how to assess novice youths' learning of programming in an open-ended, project-based learning environment. Our goal is to combine analysis of frequent, automated snapshots of programming (e.g., "big" data) within the "thick" social context of kids? learning for deeper insights into their programming trajectories. This paper focuses on the first stage of this endeavor: the development of exploratory quantitative measures of youths? learning of computer science concepts. Analyses focus on kids? learning in a series of three Scratch Camps where 64 campers aged 10-13 used Scratch 2.0 to make a series of creative projects over 30 hours in five days. In the discussion we consider the highlights of the insights-and blind spots-of each data source with regard to youths' learning.

Computer games have become a highly popular form of entertainment and have had a large impact on how University students spend their leisure time. Due to their highly motivating properties computer games have come to the attention of educationalists who wish to exploit these highly desirable properties for educational purposes. Several studies have been performed looking at motivations for playing computer games in a general context and in a Higher Education (HE) context. These studies did not focus on the differences in motivations between online and offline game players. Equally the studies did not look at the differences in motivations of people who prefer single player games and people who prefer multiplayer games. If games-based learning is to become a recognised teaching approach then such motivations for playing computer games must be better understood. This paper presents the combined analysis of three studies at HE level, performed over a four year period from 2005 to 2009. The paper focuses on differences of motivations in relation to single player/multiplayer preference and online/ offline game participation. The study found that challenge is the top ranking motivation and recognition is the lowest ranking motivation for playing computer games in general. Challenge is also the top ranking motivation for playing games in HE while fantasy and recognition are the lowest ranking motivations for playing games in HE. Multiplayer gamers derive more competition, cooperation, recognition, fantasy and curiosity from playing games and online gamers derive more challenge, cooperation, recognition and control from playing games. Multiplayer gamers and online gamers ranked competition, cooperation and recognition significantly more important for playing games in HE than single players and offline participants. 2011 Elsevier Ltd. All rights reserved.

In this paper we introduce a novel computational technique of extraction of personality traits (HEXACO) of employees from Enterprise Social Media posts. We deal with challenges such as not being able to use existing survey instruments for scoring and not being able to directly use existing psychological studies on written text due to lack of overlapping words between the existing dictionary and words used in Enterprise Social Media. Using our approach we are able to infer personality traits (HEXACO) from posts and find better coverage and usage of the extended dictionary.

Online social media, such as Reddit, has become an important resource to share personal experiences and communicate with others. Among other personal information, some social media users communicate about mental health problems they are experiencing, with the intention of getting advice, support or empathy from other users. Here, we investigate the language of Reddit posts specific to mental health, to define linguistic characteristics that could be helpful for further applications. The latter include attempting to identify posts that need urgent attention due to their nature, e.g. when someone announces their intentions of ending their life by suicide or harming others. Our results show that there are a variety of linguistic features that are discriminative across mental health user communities and that can be further exploited in subsequent classification tasks. Furthermore, while negative sentiment is almost uniformly expressed across the entire data set, we demonstrate that there are also condition-specific vocabularies used in social media to communicate about particular disorders. Source code and related materials are available from: https: //github.com/gkotsis/ reddit-mental-health.

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.

With recent developments and advances in distance learning and MOOCs, the amount of open educational videos on the Internet has grown dramatically in the past decade. However, most of these videos are lengthy and lack of high-quality indexing and annotations, which triggers an urgent demand for efficient and effective tools that facilitate video content navigation and exploration. In this paper, we propose a novel visual navigation system for exploring open educational videos. The system tightly integrates multimodal cues obtained from the visual, audio and textual channels of the video and presents them with a series of interactive visualization components. With the help of this system, users can explore the video content using multiple levels of details to identify content of interest with ease. Extensive experiments and comparisons against previous studies demonstrate the effectiveness of the proposed system.