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

This study explores the application of data mining techniques to forecast student performance on secondary education placement tests, a critical factor in academic success. By analyzing a comprehensive dataset from the Secondary Education Transition System in Turkey, we aimed to identify the key determinants influencing student outcomes on these tests. Our findings revealed that the C5 decision tree algorithm outperformed other models with a high accuracy of 95% on the validation set. Support vector machines (91% accuracy) and artificial neural networks (89% accuracy) followed closely, while logistic regression models lagged behind with an overall accuracy of 82%. Sensitivity analysis of these predictive models highlighted several significant factors: prior test experience, scholarship status, number of siblings, and previous grade point averages emerged as the most influential predictors of placement test scores. This research underscores the potential of data mining in understanding and potentially enhancing educational outcomes, a crucial consideration for educators and policymakers.

Data mining [Chen et al. 1996] is the process of extracting interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from large information repositories such as: relational database, data warehouses, XML repository, etc. Also data mining is known as one of the core processes of Knowledge Discovery in Database (KDD). Many people take data mining as a synonym for another popular term, Knowledge Discovery in Database (KDD). Alternatively other people treat Data Mining as the core process of KDD. The KDD processes are shown in Figure 1 [Han and Kamber 2000]. Usually there are three processes. One is called preprocessing, which is executed before data mining techniques are applied to the right data. The preprocessing includes data cleaning, integration, selection and transformation. The main process of KDD is the data mining process, in this process different algorithms are applied to produce hidden knowledge. After that comes another process called postprocessing, which evaluates the mining result according to users’ requirements and domain knowledge. Regarding the evaluation results, the knowledge can be presented if the result is satisfactory, otherwise we have to run some or all of those processes again until we get the satisfactory result. The actually processes work as follows. First we need to clean and integrate the databases. Since the data source may come from different databases, which may have some inconsistences and duplications, we must clean the data source by removing those noises or make some compromises. Suppose we have two different databases, different words are used to refer the same thing in their schema. When we try to integrate the two sources we can only choose one of them, if we know that they denote the same thing. And also real world data tend to be incomplete and noisy due to the manual input mistakes. The integrated data sources can be stored in a database, data warehouse or other repositories. As not all the data in the database are related to our mining task, the second process is to select task related data from the integrated resources and transform them into a format that is ready to be mined. Suppose we want to find which items are often purchased together in a supermarket, while the database that records the purchase history may contains customer ID, items bought, transaction time, prices, number of each items and so on, but for this specific task we only need items bought. After selection of relevant data, the database that we are going to apply our data mining techniques to will be much smaller, consequently the whole process will be

I have recently completed my third year of study in the Educational Psychology and Educational Technology doctoral program at Michigan State University. I have successfully completed all of my coursework and comprehensive/qualifying exams. I will be proposing my dissertation study in the Fall semester of 2016 and expect to defend my dissertation in the Fall semester of 2017. My prior research has been focused on issues related to computational thinking, creativity, and computer science education. I am currently developing my literature review and honing my core research questions. I hope to subsequently develop my research methods and measures more fully, with plans to begin fieldwork in Fall of 2016.

Credit scoring is a decision tool used by organizations to grant or reject credit requests from their customers. Series of artificial intelligent and traditional approaches have been used to building credit scoring model and credit risk evaluation. Despite being ranked amongst the top 10 algorithm in Data mining, Naïve Bayesian algorithm has not been extensively used in building credit score cards. Using demographic and material indicators as input variables, this paper investigate the ability of Bayesian classifier towards building credit scoring model in banking sector.

Organizations have been collecting data for decades, building massive data warehouses in which to store the data. Even though this data is available, very few of these organizations have been able to realize the actual value stored in it. The question these organizations are asking is how to extract meaningful data and uncover patterns and relationship from their databases. This paper presents a study of regression analysis for use in stock price prediction. Data were obtained from the daily official list of the prices of all shares traded on the stock exchange published by the Nigerian Stock Exchange using banking sector of Nigerian economy with three banks namely:First Bank of Nigeria Plc, Zenith Bank Plc, and Skye Bank Plc to build a database. A data mining software tool was used to uncover patterns and relationships and also to extract values of variables from the database to predict the future values of other variables through the use of time series data that employed moving average method. The tools were found capable technique to describe the trends of stock market prices and predict the future stock market prices of three banks sampled.

Distributed Data Mining (DDM) has become one of the promising areas of Data Mining. DDM techniques include classifier approach and agent-approach. Classifier approach plays a vital role in mining distributed data, having homogeneous and heterogeneous approaches depend on data sites. Homogeneous classifier approach involves ensemble learning, distributed association rule mining, meta-learning and knowledge probing. Heterogeneous classifier approach involves collective principle component analysis, distributed clustering, collective decision tree and collective bayesian learning model. In this paper, classifier approach for DDM is summarized and an architectural model based on clustered-collaborative filtering for Electronic Health Records (EHR) is proposed.

We propose the PeerRank method for peer assessment. This constructs a grade for an agent based on the grades proposed by the agents evaluating the agent. Since the grade of an agent is a measure of their ability to grade correctly, the PeerRank method weights grades by the grades of the grading agent. The PeerRank method also provides an incentive for agents to grade correctly. As the grades of an agent depend on the grades of the grading agents, and as these grades themselves depend on the grades of other agents, we define the PeerRank method by a fixed point equation similar to the PageRank method for ranking web-pages.

Massive Online Open Courses have the potential to revolutionize higher education with their wide outreach and accessibility, but they require instructors to come up with scalable alternates to traditional student evaluation. Peer grading -- having students assess each other -- is a promising approach to tackling the problem of evaluation at scale, since the number of "graders" naturally scales with the number of students. However, students are not trained in grading, which means that one cannot expect the same level of grading skills as in traditional settings. Drawing on broad evidence that ordinal feedback is easier to provide and more reliable than cardinal feedback [5, 38, 29, 9], it is therefore desirable to allow peer graders to make ordinal statements (e.g. "project X is better than project Y") and not require them to make cardinal statements (e.g. "project X is a B-"). Thus, in this paper we study the problem of automatically inferring student grades from ordinal peer feedback, as opposed to existing methods that require cardinal peer feedback. We formulate the ordinal peer grading problem as a type of rank aggregation problem, and explore several probabilistic models under which to estimate student grades and grader reliability. We study the applicability of these methods using peer grading data collected from a real class --- with instructor and TA grades as a baseline --- and demonstrate the efficacy of ordinal feedback techniques in comparison to existing cardinal peer grading methods. Finally, we compare these peer-grading techniques to traditional evaluation techniques.

Choosing the right career can be difficult for students, because they have to take into considerations many elements, in order to be on the best path. Several studies in many disciplines have cooperate to help students making their career decision. The purpose of this work is to invest the subject of school orientation, identifying its background and studying some related works. This paper aims also to define the student's profile, and conceiving basis of a recommendation system that will play the advisor role in students helping to choose their career path.

Data Mining is the extraction of hidden predictive information from large databases. The process of discovering interesting, useful, nontrivial patterns from large spatial datasets is called spatial data mining. When time gets associated it becomes spatio temporal data mining. The study of spatio temporal data mining is of great concern for the study of mobile phone sensed data. Reality Mining is defined as the study of human social behavior based on mobile phone sensed data. It is based on the data collected by sensors in mobile phones, security cameras, RFID readers, etc. All allow for the measurement of human physical and social activity. In this paper Netviz, Gephi and Weka tools have been used to convert and analyze the Facebook. Further, analysis of a reality mining dataset is also presented.

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.


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

An Overview of Data Mining Techniques for Predictive Analytics in Higher Education

This comprehensive overview delves into the application of data mining techniques for predictive analytics specifically within the higher education sector, aiming to forecast students' academic performance, particularly their grade point averages (GPAs) at graduation. The focus is on introducing various data mining methodologies and their potential to transform raw data into valuable insights, enabling educators and administrators to make informed decisions. Data mining, often referred to as knowledge discovery in databases (KDD), involves the process of discovering patterns, trends, and correlations within large datasets. In the context of higher education, this can include student demographics, academic records, and behavioral data. By utilizing sophisticated algorithms and models, data mining can uncover predictive indicators of academic success or challenges, allowing for early intervention strategies. This document provides an introduction to data mining, highlighting its relevance and practical applications in predicting students' GPAs at graduation, thereby enhancing student success and institutional effectiveness. Universities and colleges can leverage data mining to identify at-risk students early on, implement targeted support programs, and optimize resource allocation. The techniques discussed include classification, regression, clustering, and association rule learning, each tailored to uncover specific aspects of student performance. Through case studies and real-world examples, this overview demonstrates how data mining is revolutionizing educational practices by providing actionable insights into student outcomes.

Creating an integrative research framework that extends a model frequently used in the Information Systems field, the Technology Acceptance Model, together with variables used in the Education field, this empirical study investigates the factors influencing student performance as reflected by their final course grade. The Technology Acceptance Model explains computer acceptance in general terms. The model measures the impact of external variables on internal beliefs, attitudes, and intentions. Perceived Usefulness and Perceived Ease of Use, two main constructs in the model, refer to an individual’s perception of how the adoption of a new technology will increase their efficiency, and the individual’s perception of how easy the technology will be to use. The lower the perceived effort is, the easier the technology will be to adopt. Thus, Perceived Usefulness, Perceived Ease of Use, Computer Self-Efficacy, and Computer Anxiety were measured to determine their effect on student performance.

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.

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].

The successful application of data mining in highly visible fields like e-business, marketing and retail have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Among these sectors that are just discovering data mining are the fields of medicine and public health. This research paper provides a survey of current techniques of KDD, using data mining tools for healthcare and public health. It also discusses critical issues and challenges associated with data mining and healthcare in general. The research found a growing number of data mining applications, including analysis of health care centers for better health policy-making, detection of disease outbreaks and preventable hospital deaths, and detection of fraudulent insurance claims.

Data mining is usually defined as searching,analyzing and sifting through large amounts of data to find relationships, patterns, or any significant statistical correlation. Spatial Data Mining (SDM) is the process of discovering interesting, useful, non-trivial patterns information or knowledge from large spatial datasets.Extracting interesting and useful patterns from spatial datasets must be more difficult than extracting the corresponding patterns from traditional numeric or categorical data due to the complexity of spatial data types, spatial relationships, and spatial auto-correlation.Emphasized overviewed the unique features that distinguish spatial data mining from classical Data Mining, and presents major accomplishments of spatial Data Mining research. Extracting interesting patterns and rules from spatial datasets, such as remotely sensed imagery and associated ground data, can be of importance in precision agriculture, community planning,resource discovery and other areas.

Data Mining is the extraction of hidden predictive information from large databases. The process of discovering interesting, useful, nontrivial patterns from large spatial datasets is called spatial data mining. When time gets associated it becomes spatio temporal data mining. The study of spatio temporal data mining is of great concern for the study of mobile phone sensed data. Reality Mining is defined as the study of human social behavior based on mobile phone sensed data. It is based on the data collected by sensors in mobile phones, security cameras, RFID readers, etc. All allow for the measurement of human physical and social activity. In this paper Netviz, Gephi and Weka tools have been used to convert and analyze the Facebook. Further, analysis of a reality mining dataset is also presented.

The development of data-mining applications such as classification and clustering has been applied to large scale data. In this research, we present comparative study of different classification techniques using three data mining tools named WEKA, TANAGRA and MATLAB. The aim of this paper is to analyze the performance of different classification techniques for a set of large data. The algorithm or classifiers tested are Multilayer Perceptron, Bayes Network, J48graft (c4.5), Fuzzy Lattice Reasoning (FLR), NaiveBayes, JRip (RIPPER), Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference Systems(ANFIS). A fundamental review on the selected technique is presented for introduction purposes. The diabetes data with a total instance of 768 and 9 attributes (8 for input and 1 for output) will be used to test and justify the differences between the classification methods or algorithms. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications.

The severity of a reported bug is a critical factor in deciding how soon it needs to be fixed. Unfortunately, while clear guidelines exist on how to assign the severity of a bug, it remains an inherent manual process left to the person reporting the bug. In this paper we investigate whether we can accurately predict the severity of a reported bug by analyzing its textual description using text mining algorithms. Based on three cases drawn from the open-source community (Mozilla, Eclipse and GNOME), we conclude that given a training set of sufficient size (approximately 500 reports per severity), it is possible to predict the severity with a reasonable accuracy (both precision and recall vary between 0.65–0.75 with Mozilla and Eclipse; 0.70–0.85 in the case of GNOME).

OBJECTIVES
The intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)'s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model.

METHODS
The models were built on ICU data collected regarding 38,474 admissions to the UKH between January 1998 and September 2007. The first 24 hours of the ICU admission data were used, including patient demographics, admission information, physiology data, chronic health items, and outcome information.

RESULTS
Only 15 study variables were identified as significant for inclusion in the model development. The DT algorithm slightly outperformed (AUC, 0.892) the other data mining techniques, followed by the ANN (AUC, 0.874), and SVM (AUC, 0.876), compared to that of the APACHE III performance (AUC, 0.871).

CONCLUSIONS
With fewer variables needed, the machine learning algorithms that we developed were proven to be as good as the conventional APACHE III prediction.

Student engagement is a key concept in contemporary education, where it is valued as a goal in its own right. In this paper we explore approaches for automatic recognition of engagement from students' facial expressions. We studied whether human observers can reliably judge engagement from the face; analyzed the signals observers use to make these judgments; and automated the process using machine learning. We found that human observers reliably agree when discriminating low versus high degrees of engagement (Cohen's κ = 0.96). When fine discrimination is required (four distinct levels) the reliability decreases, but is still quite high ( κ = 0.56). Furthermore, we found that engagement labels of 10-second video clips can be reliably predicted from the average labels of their constituent frames (Pearson r=0.85), suggesting that static expressions contain the bulk of the information used by observers. We used machine learning to develop automatic engagement detectors and found that for binary classification (e.g., high engagement versus low engagement), automated engagement detectors perform with comparable accuracy to humans. Finally, we show that both human and automatic engagement judgments correlate with task performance. In our experiment, student post-test performance was predicted with comparable accuracy from engagement labels ( r=0.47) as from pre-test scores ( r=0.44).


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

This paper explores the utilization of rule templates and visualization techniques to simplify the process of identifying significant association rules from extensive datasets. Association rules, as defined by Agrawal, Imielinski, and Swami, are formulated as: "For 90% of the rows in a given dataset, if a row contains the value 1 in the columns designated by set W, then it will also contain a 1 in column B." While effective algorithms exist for the discovery of such rules from voluminous data, the sheer quantity of rules generated can pose a significant challenge for users trying to sift through and identify the most pertinent ones. To address this issue, we introduce a straightforward concept of rule templates, which enables users to easily specify the characteristics of rules they consider interesting. We further demonstrate the application of visualization tools in conjunction with rule templates, providing concrete examples of how visualization can enhance the discovery and interpretation of association rules. This integrated approach not only streamlines the identification of significant rules but also enriches the user's understanding of the data and the insights it can offer.

**Question**: The Impact of Social Media on Consumer Behavior: An Empirical Study.

**Documents**: The Role of Social Media in Online Buying Decisions
This study examines the role of social media in online buying decisions. We collected data from 400 online consumers and used structural equation modeling to test the hypotheses. The results show that social media has a significant impact on online buying decisions. Social media influences online buying decisions through three main channels: information search, social comparison, and social influence. The study also finds that the impact of social media on online buying decisions is mediated by consumer trust and moderated by consumer involvement.

**Similar Documents**: This research delves into the pivotal role social media plays in shaping online purchasing decisions. Drawing from a comprehensive dataset of 400 online shoppers, we employed structural equation modeling to rigorously assess our hypotheses. Our findings unequivocally highlight the profound effect of social media on online purchase decisions, channeling through three primary mechanisms: information seeking, social comparison, and social persuasion. Notably, the research reveals that the influence of social media on online buying choices is significantly mediated by consumer trust and further nuanced by consumer engagement levels. This study underscores the complex interplay between social media, consumer psychology, and online purchasing behavior, offering valuable insights for marketers and e-commerce platforms aiming to optimize their strategies in the digital age.

In this work we address the task of computerassisted assessment of short student answers. We combine several graph alignment features with lexical semantic similarity measures using machine learning techniques and show that the student answers can be more accurately graded than if the semantic measures were used in isolation. We also present a first attempt to align the dependency graphs of the student and the instructor answers in order to make use of a structural component in the automatic grading of student answers.

A study was made in those region that expose major area of machine learning techniques which available in data mining. There are lots of Research work is analysized in agriculture field. It is a smart techniques is needed to find the resolution over the modern method. Cotton is an significant beneficial harvest and extensively traded commodity across the world. Its yield is insightful to weather, soil as well as organization practices. This study presents a concise idea of several of the broadly capitalized machine learning technique over agricultural domain. Among the algorithms like Navie bayes, j48 ,MLP,Randomforest,Random tree, I have founded J48 gives better result over the other algorithms using cotton seed quality accuracy is best.

Automated Essay Scoring 2 Introduction This chapter documents the advent and rise of automated essay scoring (AES) as a means of both assessment and instruction. The first section discusses what AES is, how it works, and who the major purveyors of the technology are. The second section describes outgrowths of the technology as it applies to ongoing projects in measurement and education. In 1973, the late Ellis Page and colleagues at the University of Connecticut programmed the first successful automated essay scoring engine, " Project Essay Grade (PEG) " (1973). The technology was foretold some six years earlier in a landmark Phi Delta Kappan article entitled, " The Imminence of Grading Essays by Computer " (Page, 1966). At the time the article was provocative and a bit outrageous, though in hindsight, it can only be deemed prophetic. As a former high school English teacher , Page was convinced that students would benefit greatly by having access to technology that would provide quick feedback on their writing. He also realized that the greatest hindrance to having secondary students write more was the requirement that, ultimately, a teacher had to review stacks of papers. While PEG produced impressive results, the technology of the time was too primitive to make it a practical application. Text had to be typed on IBM 80-column punched cards and read into a mainframe computer before it could be evaluated. As a consequence, the technology sat dormant until the early 1990s and was revitalized with the confluence of two technological developments: microcomputers and the Internet. Microcomputers permitted the generation of electronic text from a regular keyboard and the internet provided a universal platform to submit text for review Automated Essay Scoring 3 Automated essay scoring is a measurement technology in which computers evaluate written work (Shermis & Burstein, 2003). Most of the initial applications have 2002). Computers do not " understand " the written text being evaluated. So, for example, the computer would not " get " the following joke. Q-When is a door not a door? A-When it is ajar. Unlike humans, a computer cannot interpret the play on words, and infer that the predicate in the answer (i.e., " ajar ") is being cleverly used as a noun (i.e., " a jar "). What the computer does in an AES context is to analyze the written text into its observable components. Different AES systems evaluate different …

Data Mining is the extraction of hidden predictive information from large databases. The process of discovering interesting, useful, nontrivial patterns from large spatial datasets is called spatial data mining. When time gets associated it becomes spatio temporal data mining. The study of spatio temporal data mining is of great concern for the study of mobile phone sensed data. Reality Mining is defined as the study of human social behavior based on mobile phone sensed data. It is based on the data collected by sensors in mobile phones, security cameras, RFID readers, etc. All allow for the measurement of human physical and social activity. In this paper Netviz, Gephi and Weka tools have been used to convert and analyze the Facebook. Further, analysis of a reality mining dataset is also presented.

Quite a large number of data mining techniques employed in financial fraud detection (FFD) are seen to be classification techniques. In this paper, we developed an algorithm to find the features of classification techniques (or method) that so much place it (classification techniques) in the heart of researchers in their various efforts in the study of insurance frauds detection. We also got to know the characteristics of insurance frauds data that made data mining classification techniques so much attracted to it (insurance data).

A decision tree is a outstanding method for the data mining. In intrusion detection systems (IDSs), the data mining techniques are useful to detect the attack especially in anomaly detection. For the decision tree, we use the DARPA 98 Lincoln Laboratory Evaluation Data Set (DARPA Set) as the training data set and the testing data set. KDD 99 Intrusion Detection data set is also based on the DARPA Set. These three entities are widely used in IDSs. Hence, we describe the total process to generate the decision tree learned from the DARPA Sets. In this paper, we also evaluate the effective value of the decision tree as the data mining method for the IDSs, and the DARPA Set as the learning data set for the decision trees.

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.

Students' dropout rate is a key metric in online and open distance learning courses. We propose a time-series classification method to construct data based on students' behaviour and activities on a number of online distance learning modules. Further, we propose a dropout prediction model based on the time series forest (TSF) classification algorithm. The proposed predictive model is based on interaction data and is independent of learning objectives and subject domains. The model enables prediction of dropout rates without the requirement for pedagogical experts. Results show that the prediction accuracy on two selected datasets increases as the portion of data used in the model grows. However, a reasonable prediction accuracy of 0.84 is possible with only 5% of the dataset processed. As a result, early prediction can help instructors design interventions to encourage course completion before a student falls too far behind.

Data mining (DM) methodology has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the data used by researchers. This study has proposed a new procedure, based on expanded RFM model by including one additional parameter, joining WRFM-based method to K-means algorithm applied in DM with K-optimum according to Davies– Bouldin Index, and then classifying customer product loyalty in under B2B concept. The developed methodology has been implemented for SAPCO Co. in Iran. The result shows a tremendous capability to the firm to assess his customer loyalty in marketing strategy designed by this company in comparing with random selection commonly used by most companies in Iran. 2009 Elsevier Ltd. All rights reserved.

Predicting when a person might be frustrated can provide an intelligent system with important information about when to initiate interaction. For example, an automated Learning Companion or Intelligent Tutoring System might use this information to intervene, providing support to the learner who is likely to otherwise quit, while leaving engaged learners free to discover things without interruption. This paper presents the first automated method that assesses, using multiple channels of affect-related information, whether a learner is about to click on a button saying ‘‘I’m frustrated.’’ The new method was tested on data gathered from 24 participants using an automated Learning Companion. Their indication of frustration was automatically predicted from the collected data with 79% accuracy ðchance 1⁄4 58%Þ. The new assessment method is based on Gaussian process classification and Bayesian inference. Its performance suggests that non-verbal channels carrying affective cues can help provide important information to a system for formulating a more intelligent response. r 2007 Elsevier Ltd. All rights reserved.


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

This study explores the critical role of language choice in classroom teaching and its influence on student attendance and engagement. In the rich educational history of India, the classroom has been the primary setting for knowledge dissemination, with teachers delivering lessons and students actively absorbing the material. While academic performance is often gauged through examination scores, the selection of an appropriate teaching language and the preparation of class notes are pivotal factors in enhancing student comprehension and participation.

The central focus of this research is to quantify the impact of language on student presence and involvement in the classroom. By employing data mining techniques, specifically association rule analysis, the study aims to uncover the support, confidence, and interestingness levels associated with the use of a suitable language and regular attendance. This approach not only highlights the significance of language in education but also underscores the importance of consistent student presence for optimal learning outcomes. Through this analysis, educators can gain valuable insights into tailoring their teaching methods to better meet the linguistic and learning needs of their students, ultimately improving academic performance and engagement.

Massive Open Online Courses (MOOCs) have a high attrition rate: most students who register for a course do not complete it. By examining a student's history of actions during a course, we can predict whether or not they will drop out in the next week, facilitating interventions to improve retention. We compare predictions resulting from several modeling techniques and several features based on different student behaviors. Our best predictor uses a Hidden Markov Model (HMM) to model sequences of student actions over time, and encodes several continuous features into a single discrete observable state using a simple crossproduct method. It yielded an ROC AUC (Receiver Operating Characteristic Area Under the Curve score) of 0.710, considerably better than a random predictor. We also use simpler HMM models to derive information about which student behaviors are most salient in determining student retention.

The banking industry has undergone various changes in the way they conduct the business and focus on modern technologies to compete the market. The banking industry has started realizing the importance of creating the knowledge base and its utilization for the benefits of the bank in the area of strategic planning to survive in the competitive market. In the modern era, the technologies are advanced and it facilitates to generate, capture and store data are increased enormously. Data is the most valuable asset, especially in financial industries. The value of this asset can be evaluated only if the organization can extract the valuable knowledge hidden in raw data. The increase in the huge volume of data as a part of day to day operations and through other internal and external sources, forces information technology industries to use technologies like data mining to transform knowledge from data. Data mining technology provides the facility to access the right information at the right time from huge volumes of raw data. Banking industries adopt the data mining technologies in various areas especially in customer segmentation and profitability, Predictions on Prices/Values of different investment products, money market business, fraudulent transaction detections, risk predictions, default prediction on pricing. It is a valuable tool which identifies potentially useful information from large amount of data, from which organization can gain a clear advantage over its competitors. This study shows the significance of data mining technologies and its advantages in the banking and financial sectors.

1 Department of Decision Sciences and Information Management, K.U.Leuven Naamsestraat 69, B-3000 Leuven, Belgium {David.Martens;Johan.Huysmans; Bart.Baesens;Jan.Vanthienen}@econ.kuleuven.be 2 School of Computing, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore rudys@comp.nus.edu.sg 3 University of Southampton, School of Management, Highfield Southampton, SO17 1BJ, UK Bart@soton.ac.uk

Answering questions correctly from standardized eighth-grade science tests is itself a test of machine intelligence.

Students' dropout rate is a key metric in online and open distance learning courses. We propose a time-series classification method to construct data based on students' behaviour and activities on a number of online distance learning modules. Further, we propose a dropout prediction model based on the time series forest (TSF) classification algorithm. The proposed predictive model is based on interaction data and is independent of learning objectives and subject domains. The model enables prediction of dropout rates without the requirement for pedagogical experts. Results show that the prediction accuracy on two selected datasets increases as the portion of data used in the model grows. However, a reasonable prediction accuracy of 0.84 is possible with only 5% of the dataset processed. As a result, early prediction can help instructors design interventions to encourage course completion before a student falls too far behind.

In Web Usage Mining (WUM), Classification technique is useful in web user prediction, where user’s browsing behavior is predicted. In Web prediction, classification is done first to predict the next Web pages set that a user may visit based on history of previously visited pages. Predicting user’s behavior effectively is important in various applications like recommendation systems, smart phones etc. Currently various prediction systems are available based on Markov model, Association Rule Mining (ARM), classification techniques, etc. In Markov prediction model it cannot predict for a session that was not observed previously in the training set and prediction time is also constant. In ARM, Apriori algorithm requires more time to generate item sets. This paper presents review of various prediction models used to predict web users browsing behavior. Key Words— WUM, classification, Browsing behavior, prediction, ARM, session

A major opportunity for NLP to have a realworld impact is in helping educators score student writing, particularly content-based writing (i.e., the task of automated short answer scoring). A major challenge in this enterprise is that scored responses to a particular question (i.e., labeled data) are valuable for modeling but limited in quantity. Additional information from the scoring guidelines for humans, such as exemplars for each score level and descriptions of key concepts, can also be used. Here, we explore methods for integrating scoring guidelines and labeled responses, and we find that stacked generalization (Wolpert, 1992) improves performance, especially for small training sets.

This paper presents a use case of data mining for sales forecasting in retail demand and sales prediction. In particular, the Extreme Gradient Boosting algorithm is used to design a prediction model to accurately estimate probable sales for retail outlets of a major European Pharmacy retailing company. The forecast of potential sales is based on a mixture of temporal and economical features including prior sales data, store promotions, retail competitors, school and state holidays, location and accessibility of the store as well as the time of year. The model building process was guided by common sense reasoning and by analytic knowledge discovered during data analysis and definitive conclusions were drawn. The performances of the XGBoost predictor were compared with those of more traditional regression algorithms like Linear Regression and Random Forest Regression. Findings not only reveal that the XGBoost algorithm outperforms the traditional modeling approaches with regard to prediction accuracy, but it also uncovers new knowledge that is hidden in data which help in building a more robust feature set and strengthen the sales prediction model.

Our team intends to develop a recommendation system for job seekers based on the information of current employees in big companies. Several models are implemented to achieve over 60% success rate in classifying employees, and we use these models to help job seekers identify their best fitting company.

The study tested the extent to which parental social support predicted college grade point average among undergraduate students. A sample of 418 undergraduates completed the Social Provisions Scale--Parent Form (C.E. Cutrona, 1989) and measures of family conflict and achievement orientation. American College Testing Assessment Program college entrance exam scores (ACT; American College Testing Program, 1986) and grade point average were obtained from the university registrar. Parental social support, especially reassurance of worth, predicted college grade point average when controlling for academic aptitude (ACT scores), family achievement orientation, and family conflict. Support from parents, but not from friends or romantic partners, significantly predicted grade point average. Results are interpreted in the context of adult attachment theory.


Question: Do students employ retrieval methods during independent study when utilizing metacognitive strategies?

This study explores the impact of open-book and closed-book testing on students' long-term retention and their ability to predict their own recall abilities. In two experiments, participants studied text material and then either revisited the material or took an open-book or closed-book test. Both types of tests, when accompanied by feedback, were found to significantly improve long-term retention compared to restudy conditions or testing without feedback. While open-book testing initially led to better performance, this advantage did not last, and both open-book and closed-book testing ultimately resulted in similar levels of delayed recall. Surprisingly, participants anticipated better recall after repeated study sessions, even though the data showed that testing was more effective in enhancing long-term recall than restudy. These findings underscore the effectiveness of the testing effect, which applies equally to open-book and closed-book scenarios, and reveal that students often underestimate the benefits of testing over studying for future recall. Copyright # 2007 John Wiley & Sons, Ltd.

**Question**: How do personality traits influence the development of leadership skills in young adults?

**Documents**: The Role of Personality in Leadership: A Review and Meta-Analysis
This paper reviews the literature on the relationship between personality traits and leadership. We conducted a meta-analysis to synthesize the findings of previous studies on the association between the Big Five personality traits and leadership effectiveness. The results showed that extraversion, conscientiousness, and emotional stability were positively related to leadership effectiveness, while agreeableness and openness were not significantly related. However, when the sample was restricted to young adults, agreeableness and openness showed a positive relationship with leadership effectiveness. These findings suggest that personality traits play a significant role in leadership, with extraversion, conscientiousness, and emotional stability being particularly important. However, the relationship between personality traits and leadership may differ for young adults, with agreeableness and openness also being important for leadership effectiveness in this population.

**Similar Documents**: This paper presents a comprehensive review and meta-analysis of the existing research on the connection between personality traits and leadership abilities. Focusing on the Big Five personality dimensions, we found that extraversion, conscientiousness, and emotional stability are positively correlated with effective leadership. Surprisingly, agreeableness and openness did not show a significant relationship with leadership effectiveness in the general population. However, when the analysis was narrowed down to young adults, both agreeableness and openness were found to have a positive correlation with leadership effectiveness. These findings highlight the crucial role of personality in leadership, with extraversion, conscientiousness, and

The Internet is the largest source of information in the world. Search engines help people navigate the huge space of available data in order to acquire new skills and knowledge. In this paper, we present an in-depth analysis of sessions in which people explicitly search for new knowledge on the Web based on the log files of a popular search engine. We investigate within-session and cross-session developments of expertise, focusing on how the language and search behavior of a user on a topic evolves over time. In this way, we identify those sessions and page visits that appear to significantly boost the learning process. Our experiments demonstrate a strong connection between clicks and several metrics related to expertise. Based on models of the user and their specific context, we present a method capable of automatically predicting, with good accuracy, which clicks will lead to enhanced learning. Our findings provide insight into how search engines might better help users learn as they search.

The present research compared the relative effectiveness of an attribution strategy with a persuasion strategy in changing behavior. Study 1 attempted to teach fifth graders not to litter and to clean up after others. An attribution group was repeatedly told that they were neat and tidy people, a persuasion group was repeatedly told that they should be neat and tidy, and a control group received no treatment. Attribution proved considerably more effective in modifying behavior. Study 2 tried to discover whether similar effects would hold for a more central aspect of school performance, math achievement and self-esteem, and whether an attribution of ability would be as effective as an attribution of motivation. Repeatedly attributing to second graders either the ability or the motivation to do well in math proved more effective than comparable persuasion or no-treatment control groups, although a group receiving straight reinforcement for math problem-solving behavior also did well. It is suggested that persuasion often suffers because it involves a negative attribution (a person should be what he is not), while attribution generally gains because it disguises persuasive intent.

A second-order meta-analysis was conducted to assess the implications of using college student subjects in social science research. Four meta-analyses investigating response homogeneity (cumulative N 1 650,000) and 30 meta-analyses reporting effect sizes for 65 behavioral or psychological relationships (cumulative N 1 350,000) provided comparative data for college student subjects and nonstudent (adult) subjects for the present research. In general, responses of college student subjects were found to be slightly more homogeneous than those of nonstudent subjects.Moreover, effect sizes derived from college student subjects frequently differed from those derived from nonstudent subjects both directionally and in magnitude. Because there was no systematic pattern to the differences observed, caution must be exercised when attempting to extend any relationship found using college student subjects to a nonstudent (adult) population. The results augur in favor of, and emphasize the importance of, replicating research based on college student subjects withnonstudent subjects before attempting any generalizations.

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.

The relationship between a theory of learning and a theory of instruction is discussed. Examples are presented that illustrate how to proceed from a theoretical description of the learning process to the specification of an optimal strategy for carrying out instruction. The examples deal with fairly simple learning tasks and are admittedly of limited generality. Nevertheless, they clearly define the steps necessary for deriving and testing instructional strategies, thereby providing a set of procedures for analyzing more complex problems. The parameter-dependent optimization strategies are of particular importance because they take into account individual differences among learners as well as differences in difficulty among curriculum units. Experimental evaluations indicate that the parameter-dependent strategies lead to major gains in learning, when compared with strategies that do not take individual differences into account.

Human cognitive architecture includes a working memory of limited capacity and duration with partially separate visual and auditory channels, and an effectively infinite long-term memory holding many schemas that can vary in their degree of automation. These cognitive structures have evolved to handle information that varies in the extent to which elements can be processed successively in working memory or, because they interact, must be processed simultaneously imposing a heavy load on working memory. Cognitive load theory uses this combination of information and cognitive structures to guide instructional design. Several designs that rely heavily on visual working memory and its characteristics are discussed in this paper. Knowledge of human cognitive architecture is essential for instructional design, and visual cognition is a central aspect of human cognition. Not surprisingly, there are several instructional design effects that rely heavily on the manner in which humans visually process information. This paper discusses some relevant information structures, cognitive structures and instructional designs that rely on our knowledge of visual information processing. A. Information structures While considerable work by many researchers over several decades has been devoted to the organization of human cognitive architecture, far less effort has gone into investigating the information structures that must have driven the evolution of that architecture. Some work has been carried out by Sweller (1994) and Halford, Wilson and Phillips (1998). Sweller (1994) suggested that all information can be placed on a continuum according to the extent to which the elements that constitute the information interact. At one extreme, there is no interaction between the elements that need to be learned. They are independent. Element interactivity is low, or indeed, non-existent, and that means each element can be considered and learned serially without reference to any other element. Because elements at the low element interactivity end of the continuum do not interact with each other, there is no loss of understanding despite each element being learned individually and in isolation. Understanding is defined as the ability to process all elements that necessarily interact, simultaneously in working memory. Learning such material

Three laboratory experiments involving students' behavior and brain imaging and one randomized field experiment in a college physics class explored the importance of physical experience in science learning. We reasoned that students' understanding of science concepts such as torque and angular momentum is aided by activation of sensorimotor brain systems that add kinetic detail and meaning to students' thinking. We tested whether physical experience with angular momentum increases involvement of sensorimotor brain systems during students' subsequent reasoning and whether this involvement aids their understanding. The physical experience, a brief exposure to forces associated with angular momentum, significantly improved quiz scores. Moreover, improved performance was explained by activation of sensorimotor brain regions when students later reasoned about angular momentum. This finding specifies a mechanism underlying the value of physical experience in science education and leads the way for classroom practices in which experience with the physical world is an integral part of learning.

"Flipping" the classroom employs easy-to-use, readily accessihle technology in order to free class time from lecture. This allows for an expanded range of learning activities during class time. Using class time for active learning versus lecture provides opportunities for greater teacher-to-student mentoring, peer-to-peer collaboration and cross-disciplinary engagement. This review of literature addresses the challenges of engaging today's students in lecture-based classrooms and presents an argument for application of the "flipped classroom" model hy educators in the disciplines of family and consumer sciences.

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.

New systems are designed with improved quality or features. However, not every new system lives to its pre-development expectations when tested with end-users. The evaluation of the first version of our LOCO-Analyst tool for providing teachers with feedback on students learning activities and performance led to the enhancing the visualization features of the tool. The second evaluation of the improved tool allowed us to see how the improvements affect the user perceptions of the tool. Here, we present the qualitative results of our two evaluations. The results show that educators find the kinds of feedback implemented in the tool informative and they value the mix of textual and graphical representations of different kinds of feedback provided by the tool.


Question: Do students use retrieval practices when studying independently, regarding metacognitive strategies in learning?

The Impact of Retrieval Practice on Learning: Bridging the Gap Between Laboratory Research and Educational Practice.
Retrieval practice, a strategy that involves recalling information from memory, has been shown to significantly enhance memory retention. This phenomenon, often referred to as the testing effect, has been a focus of cognitive psychology, with recent studies exploring its application in educational contexts. We review key findings from laboratory studies that highlight the superior impact of retrieval practice on retention compared to additional study time. Moving beyond the lab, we examine how these principles can be effectively integrated into educational settings. We also explore related concepts such as dynamic testing and formative assessment, which leverage testing as a tool to foster deeper learning. While acknowledging potential negative outcomes of testing in certain scenarios, we emphasize that these are typically minor and do not overshadow the substantial benefits of retrieval practice. Regular implementation of retrieval practice in educational settings holds promise for enhancing learning outcomes across all educational levels.

**Question**: The role of social networks in the diffusion of innovations

**Documents**: How Social Networks Can Influence the Diffusion of Innovations in the Agricultural Sector.
Innovations often spread through social networks, with individuals adopting new ideas or technologies based on information and experiences shared by their social contacts. This paper explores the role of social networks in the diffusion of innovations within the agricultural sector. We examine how the structure of social networks, including the degree of interconnectedness and the presence of opinion leaders, influences the speed and pattern of innovation adoption. Using case studies and empirical data, we demonstrate that social networks play a critical role in the dissemination of information about new agricultural practices and technologies. Our findings suggest that targeted interventions aimed at leveraging social networks can accelerate the diffusion of innovations in the agricultural sector.

**Similar Documents**: The Influence of Social Networks on the Adoption of Agricultural Innovations: A Case Study.
Social networks are pivotal in the dissemination of innovations, particularly in sectors like agriculture where peer influence is significant. This study investigates how social networks affect the adoption of agricultural innovations by analyzing the structure and dynamics of these networks. We focus on the interconnectedness of farmers and the role of key influencers in facilitating the spread of new practices and technologies. Through detailed case studies and empirical analysis, we illustrate that social networks significantly impact the rate and pattern of innovation adoption. Our results indicate that by strategically engaging with social networks, the diffusion of agricultural innovations can be accelerated, leading to improved agricultural productivity and sustainability.

Four computer-based training strategies for geometrical problem solving in the domain of computer numerically controlled machinery programming were studied with regard to their effects on training performance, transfer performance, and cognitive load. A lowand a high-variability conventional condition, in which conventional practice problems had to be solved (followed by worked examples), were compared with a lowand a high-variability worked condition, in which worked examples had to be studied. Results showed that students who studied worked examples gained most from high-variability examples, invested less time and mental effort in practice, and attained better and less effort-demanding transfer performance than students who first attempted to solve conventional problems and then studied work examples.

Students in online courses generate large amounts of data that can be used to personalize the learning process and improve quality of education. In this paper, we present the Latent Skill Embedding (LSE), a probabilistic model of students and educational content that can be used to recommend personalized sequences of lessons with the goal of helping students prepare for specific assessments. Akin to collaborative filtering for recommender systems, the algorithm does not require students or content to be described by features, but it learns a representation using access traces. We formulate this problem as a regularized maximum-likelihood embedding of students, lessons, and assessments from historical student-content interactions. An empirical evaluation on large-scale data from Knewton, an adaptive learning technology company, shows that this approach predicts assessment results competitively with benchmark models and is able to discriminate between lesson sequences that lead to mastery and failure.

This study was planned to explore the impact of eLearning on the academic performance of student-teachers. The researchers' concern with e-Learning was sanctioned by the need for a teaching and learning strategy that can help institutions of learning address their problems and improve on their outcome. In this respect, the researchers conducted an experiment to find out whether student-teachers taught using the method of e-Learning (blended learning) perform better than studentteachers taught using the traditional method of teaching and learning. Findings offers new evidence that e-Learning has a significant influence on the performance of students as student-teachers taught using eLearning consistently performed better than student-teachers taught using the traditional method. Based on this result, recommendations are made to training institutions to embrace ICTs and become more flexible by adopting learning approaches that are dynamic and multidimensional as problems in education are becoming more complex.

In a series of three experimental studies, we investigate the understanding achieved by learners when studying a complex domain in computer science, that is, the subject of memory management concepts in operating systems. We use three different media combinations to explain the subject of memory management concepts - Animation and text, animation and voice and a combination of animation, voice and text. The students who had no prior knowledge on the subject where given a test on recall and transfer knowledge after viewing the treatments. This paper reports the results of the experiment which may provide useful guidance for instructional designers specifically in the area of computer science.

Although a solid understanding of fractions is foundational in mathematics, the concept of fractions remains a challenging one. Previous research suggests that multiple graphical representations (MGRs) may promote learning of fractions. Specifically, we hypothesized that providing students with MGRs of fractions, in addition to the conventional symbolic notation, leads to better learning outcomes as compared to instruction incorporating only one graphical representation. We anticipated, however, that MGRs would make the students’ task more challenging, since they must link the representations and distill from them a common concept or principle. Therefore, we hypothesized further that selfexplanation prompts would help students benefit from working with MGRs. To investigate these hypotheses, we conducted a classroom study in which 112 6grade students used intelligent tutors for fraction conversion and fraction addition. The results of the study show that students learned more with MGRs of fractions than with a single representation, but only when prompted to self-explain how the graphics relate to the symbolic fraction representations.

While laptops and other Internet accessible technologies facilitate student learning in the classroom, they also increase opportunities for interruptions from off-task social networking sites such as Facebook (FB). A small number of correlational studies have suggested that FB has a detrimental effect on learning performance, however; these studies had neglected to investigate student-engagement in the primary learning task and how this affects task-switching to goal-irrelevant FB intrusions (distractions); and how purposeful deployment of attention to FB (goal-relevant interruptions) affect lecture comprehension on such tasks. This experiment fills a gap in the literature by manipulating lecture interest-value and controls for duration of FB exposure, time of interruption, FB material and the order of FB posts. One hundred and fifty participants were randomly allocated to one of six conditions: (A) no FB intrusions, high-interest (HI) lecture; (B) no FB intrusions, low-interest (LI) lecture (C) goal-relevant FB intrusions, HI lecture (D) goal-relevant FB intrusions, LI lecture (E) goal-irrelevant FB intrusions, HI lecture (F) goal-irrelevant FB intrusions, LI lecture. As predicted, participants were more susceptible to FB distractions when the primary learning task was of low-interest. The study also found that goal-relevant FB intrusions significantly reduced HI lecture comprehension compared to the control condition (A). The results highlight the need for recourses that will help educators increase student engagement with their learning task. Implications for future research are discussed. 2014 Elsevier Ltd. All rights reserved.

In a life-long learning society, learning scenarios can be categorized into five types, which are “classroom learning”, “self-learning”, “inquiry learning”, “learning in doing” and “learning in working”. From a life-wide learning perspective, all these scenarios play vital roles for personal development. How to recognize these learning scenarios (including learning time, learning place, learning peers, learning activities, etc.) and provide the matched learning ways (including learning path, resources, peers, teachers, etc.) are the basis for smart learning environments, however few research could be found to address this problem. In order to solve this problem, we propose a conceptual framework of smart learning engine that is the core of integrated, interactive and intelligent (i) learning environments. The smart learning engine consists of three main functions. The first function is to identify data from student, teacher, subject area, and the environment using wireless sensors, the established learning resources and scenarios, and a learner modeling technology. The acquired data includes prior knowledge, theme-based context, leaner/teacher profile, physical environments, etc. The second function is to compute the best ways of learning based on the learning scenario and learning preference. In detail, this function includes modeling learner’s affective data, building knowledge structure, optimizing knowledge module, and connecting learners. The third function is to deploy personalized and adaptive strategy, resources and tools for students and teachers based on the computed results in the second function. Deploy interactive strategies, learning paces, learning resources, and delivery approaches are the core elements for this function.

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

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.


Question: Do students use retrieval methods when studying independently, regarding metacognitive strategies in their learning process?

METACOGNITIVE AWARENESS: CONCEPTUAL FRAMEWORK AND EMERGING EVIDENCE
This paper centers on our ongoing research program centered on metacognition, particularly metamemory, an area that has seen significant contributions from various researchers. Our aim is to outline the conceptual framework that has emerged from our metamemory research, discuss our research methodology, and present our previously unshared findings. It's worth noting that we will not provide a comprehensive review of our published research; instead, we will refer to it only when it is essential for the coherence of the discussion.

The conceptual framework we propose integrates various dimensions of metamemory, including metacognitive knowledge, metacognitive experience, and metacognitive regulation. Metacognitive knowledge encompasses the understanding of one's own cognitive processes, while metacognitive experience involves the monitoring of cognitive states and processes. Metacognitive regulation, on the other hand, refers to the ability to control and direct one's cognitive activities.

Our methodology involves experimental designs that enable us to assess the metacognitive strategies employed by students in various learning contexts. This includes tasks that require students to estimate their performance before and after learning activities, as well as tasks that involve self-explanation and reflection on learning processes.

The findings from our research highlight the critical role of metacognitive strategies in learning. Specifically, we have observed that students who engage in retrieval practice—recalling information from memory without the aid of external resources—tend to have better learning outcomes compared to those who do not. However, our data also reveal that retrieval practice is not a common strategy among students when studying independently. This suggests that there is a need for educational interventions to promote the use of effective metacognitive strategies, such as retrieval practice, in student learning.

In conclusion, our research on metamemory provides a comprehensive framework for understanding metacognition and its implications for learning. The insights gained from our studies underscore the importance of fostering metacognitive awareness and skills in educational settings to enhance student learning outcomes.

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.

Three laboratory experiments involving students' behavior and brain imaging and one randomized field experiment in a college physics class explored the importance of physical experience in science learning. We reasoned that students' understanding of science concepts such as torque and angular momentum is aided by activation of sensorimotor brain systems that add kinetic detail and meaning to students' thinking. We tested whether physical experience with angular momentum increases involvement of sensorimotor brain systems during students' subsequent reasoning and whether this involvement aids their understanding. The physical experience, a brief exposure to forces associated with angular momentum, significantly improved quiz scores. Moreover, improved performance was explained by activation of sensorimotor brain regions when students later reasoned about angular momentum. This finding specifies a mechanism underlying the value of physical experience in science education and leads the way for classroom practices in which experience with the physical world is an integral part of learning.

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.

While laptops and other Internet accessible technologies facilitate student learning in the classroom, they also increase opportunities for interruptions from off-task social networking sites such as Facebook (FB). A small number of correlational studies have suggested that FB has a detrimental effect on learning performance, however; these studies had neglected to investigate student-engagement in the primary learning task and how this affects task-switching to goal-irrelevant FB intrusions (distractions); and how purposeful deployment of attention to FB (goal-relevant interruptions) affect lecture comprehension on such tasks. This experiment fills a gap in the literature by manipulating lecture interest-value and controls for duration of FB exposure, time of interruption, FB material and the order of FB posts. One hundred and fifty participants were randomly allocated to one of six conditions: (A) no FB intrusions, high-interest (HI) lecture; (B) no FB intrusions, low-interest (LI) lecture (C) goal-relevant FB intrusions, HI lecture (D) goal-relevant FB intrusions, LI lecture (E) goal-irrelevant FB intrusions, HI lecture (F) goal-irrelevant FB intrusions, LI lecture. As predicted, participants were more susceptible to FB distractions when the primary learning task was of low-interest. The study also found that goal-relevant FB intrusions significantly reduced HI lecture comprehension compared to the control condition (A). The results highlight the need for recourses that will help educators increase student engagement with their learning task. Implications for future research are discussed. 2014 Elsevier Ltd. All rights reserved.

It is often said that successive generations of researchers face an increasing educational burden due to knowledge accumulation. On the other hand, technological advancement over time can improve the productivity of researchers and even change their cognitive processes. This paper presents a longitudinal study (2004–2011) of citation behavior in doctoral theses at the Massachusetts Institute of Technology’s Department of Electrical Engineering and Computer Science. It is found that the number of references cited has increased over the years. At the same time, there has been a decrease in the length of time in the doctoral program and a relative constancy in the culture of the department. This suggests that students are more productive in facing an increased knowledge burden, and indeed seem to encode prior literature as transactive memory to a greater extent, as evidenced by the greater use of older literature.

In 2 experiments, students who lacked prior knowledge about car mechanics read a passage about vehicle braking systems that either contained labeled illustrations of the systems, illustrations without labels, labels without illustrations, or no labeled illustrations. Students who received passages that contained labeled illustrations of braking systems recalled more explanative than nonexplanative information as compared to control groups, and performed better on problem solving transfer but not on verbatim recognition as compared to control groups. Results support a model of meaningful learning in which illustrations can help readers to focus their attention on explanative information in text and to reorganize the information into useful mental models.

Learning predictors for student retention is very difficult. After reviewing the literature, it is evident that there is considerable room for improvement in the current state of the art. As shown in this paper, improvements are possible if we (a) explore a wide range of learning methods; (b) take care when selecting attributes; (c) assess the efficacy of the learned theory not just by its median performance, but also by the variance in that performance; (d) study the delta of student factors between those who stay and those who are retained. Using these techniques, for the goal of predicting if students will remain for the first three years of an undergraduate degree, the following factors were found to be informative: family background and family’s social-economic status, high school GPA

We studied how specific motivational processes are related to the salience of mastery and performance goals in actual classroom settings. One hundred seventy-six students attending a junior high/high school for academically advanced students were randomly selected from one of their classes and responded to a questionnaire on their perceptions of the classroom goal orientation, use of effective learning strategies, task choices, attitudes, and causal attributions. Students who perceived an emphasis on mastery goals in the classroom reported using more effective strategies, preferred challenging tasks, had a more positive attitude toward the class, and had a stronger belief that success follows from one's effort. Students who perceived performance goals as salient tended to focus on their ability, evaluating their ability negatively and attributing failure to lack of ability. The pattern and strength of the findings suggest that the classroom goal orientation may facilitate the maintenance of adaptive motivation patterns when mastery goals are salient and are adopted by students.

Students in online courses generate large amounts of data that can be used to personalize the learning process and improve quality of education. In this paper, we present the Latent Skill Embedding (LSE), a probabilistic model of students and educational content that can be used to recommend personalized sequences of lessons with the goal of helping students prepare for specific assessments. Akin to collaborative filtering for recommender systems, the algorithm does not require students or content to be described by features, but it learns a representation using access traces. We formulate this problem as a regularized maximum-likelihood embedding of students, lessons, and assessments from historical student-content interactions. An empirical evaluation on large-scale data from Knewton, an adaptive learning technology company, shows that this approach predicts assessment results competitively with benchmark models and is able to discriminate between lesson sequences that lead to mastery and failure.

For a learning platform to be adapti ve, it needs to monitor the user in r ealtime. At the ScienceCampus Tübingen, we conduct empirical research on learning strategies, in particular with regard to system adaptivity. Our experimental Webbased adaptiv e multimedia learning environment is able to communicate with an eyetracking system in real-time, and l inks gaze data to browser-based learning content. By processing simple evaluation scripts in the browser, we can control the learning process by adapting the learning content in response to predefined conditions and user inputs. The experimental system serves as a basis for a series of psyc hologi al studies in the learning context. We have already conducted some studies with our experimental learning platform, but its devel opment is still ongoing. In particular, we are adding new feature s required for future studies. In this paper, we desc ribe the actual state of our experimental learning environment, lessons learned from its a pplication in the studies, and our plans for future devel opment.