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Li, ZhaoBin; Yee, Luna; Sauerberg, Nathaniel; Sakson, Irene; Williams, Joseph Jay; Rafferty, Anna N. – International Educational Data Mining Society, 2020
Digital educational technologies offer the potential to customize students' experiences and learn what works for which students, enhancing the technology as more students interact with it. We consider whether and when attempting to discover how to personalize has a cost, such as if the adaptation to personal information can delay the adoption of…
Descriptors: Educational Technology, Technology Uses in Education, Student Needs, Student Characteristics
Liu, Zhi; Yang, Chongyang; Rüdian, Sylvio; Liu, Sannyuya; Zhao, Liang; Wang, Tai – Interactive Learning Environments, 2019
Textual data, as a key carrier of learning feedback, is continuously produced by many students within course forums. The temporal nature of discussion requires students' emotions and concerned aspects (e.g. teaching styles, learning activities, etc.) to be dynamically tracked for understanding learning requirements. To characterize dynamics of…
Descriptors: Online Courses, Student Attitudes, Emotional Response, Models
Geigle, Chase; Zhai, ChengXiang – Journal of Educational Data Mining, 2017
Massive open online courses (MOOCs) provide educators with an abundance of data describing how students interact with the platform, but this data is highly underutilized today. This is in part due to the lack of sophisticated tools to provide interpretable and actionable summaries of huge amounts of MOOC activity present in log data. To address…
Descriptors: Large Group Instruction, Online Courses, Educational Technology, Technology Uses in Education
Ouedraogo, Boukary – Higher Education Studies, 2017
This article uses data survey on 82 teachers from the University of Ouagadougou and the model of unified theory of acceptance and use of technology (UTAUT) to assess the determinants of acceptance and educational use of ICT by teachers. The paper's outcomes show that the construct "performance expectancy" of ICT (expected utility and…
Descriptors: Foreign Countries, Models, Information Technology, Computer Attitudes
Matsumoto, Paul S.; Cao, Jiankang – Journal of Chemical Education, 2017
Computational thinking is a component of the Science and Engineering Practices in the Next Generation Science Standards, which were adopted by some states. We describe the activities in a high school chemistry course that may develop students' computational thinking skills by primarily using Excel, a widely available spreadsheet software. These…
Descriptors: Secondary School Science, High School Students, Computation, Thinking Skills
Streeter, Matthew – International Educational Data Mining Society, 2015
We show that student learning can be accurately modeled using a mixture of learning curves, each of which specifies error probability as a function of time. This approach generalizes Knowledge Tracing [7], which can be viewed as a mixture model in which the learning curves are step functions. We show that this generality yields order-of-magnitude…
Descriptors: Probability, Error Patterns, Learning Processes, Models
Eliasquevici, Marianne Kogut; da Rocha Seruffo, Marcos César; Resque, Sônia Nazaré Fernandes – International Journal of Distance Education Technologies, 2017
This article presents a study on the variables promoting student retention in distance undergraduate courses at Federal University of Pará, aiming to help school managers minimize student attrition and maximize retention until graduation. The theoretical background is based on Rovai's Composite Model and the methodological approach is conditional…
Descriptors: Distance Education, Case Studies, Academic Persistence, Undergraduate Students
MacLellan, Christopher J.; Liu, Ran; Koedinger, Kenneth R. – International Educational Data Mining Society, 2015
Additive Factors Model (AFM) and Performance Factors Analysis (PFA) are two popular models of student learning that employ logistic regression to estimate parameters and predict performance. This is in contrast to Bayesian Knowledge Tracing (BKT) which uses a Hidden Markov Model formalism. While all three models tend to make similar predictions,…
Descriptors: Factor Analysis, Regression (Statistics), Knowledge Level, Markov Processes
Dillenbourg, Pierre – International Journal of Artificial Intelligence in Education, 2016
How does AI&EdAIED today compare to 25 years ago? This paper addresses this evolution by identifying six trends. The trends are ongoing and will influence learning technologies going forward. First, the physicality of interactions and the physical space of the learner became genuine components of digital education. The frontier between the…
Descriptors: Artificial Intelligence, Educational Trends, Trend Analysis, Educational Technology
Andjelic, Svetlana; Cekerevac, Zoran – Education and Information Technologies, 2014
This article presents the original model of the computer adaptive testing and grade formation, based on scientifically recognized theories. The base of the model is a personalized algorithm for selection of questions depending on the accuracy of the answer to the previous question. The test is divided into three basic levels of difficulty, and the…
Descriptors: Computer Assisted Testing, Educational Technology, Grades (Scholastic), Test Construction
Marques, Alice; Belo, Orlando – Electronic Journal of e-Learning, 2011
Nowadays, Web based platforms are quite common in any university, supporting a very diversified set of applications and services. Ranging from personal management to student evaluation processes, Web based platforms are doing a great job providing a very flexible way of working, promote student enrolment, and making access to academic information…
Descriptors: Student Evaluation, Markov Processes, Profiles, Web Sites
Salahli, Mehmet Ali; Özdemir, Muzaffer; Yasar, Cumali – International Education Studies, 2013
One of the most important factors for improving the personalization aspects of learning systems is to enable adaptive properties to them. The aim of the adaptive personalized learning system is to offer the most appropriate learning path and learning materials to learners by taking into account their profiles. In this paper, a new approach to…
Descriptors: Individualized Instruction, Electronic Learning, Educational Technology, Profiles
Kottonau, Johannes – Journal of Chemical Education, 2011
Effectively teaching the concepts of osmosis to college-level students is a major obstacle in biological education. Therefore, a novel computer model is presented that allows students to observe the random nature of particle motion simultaneously with the seemingly directed net flow of water across a semipermeable membrane during osmotic…
Descriptors: Models, Probability, Internet, Misconceptions
Fong, Soon Fook; Por, Fei Ping; Tang, Ai Ling – Turkish Online Journal of Educational Technology - TOJET, 2012
The purpose of this study was to investigate the effects of multiple simulation presentation in interactive multimedia are on the achievement of students with different levels of anxiety in the learning of Probability. The interactive multimedia courseware was developed in two different modes, which were Multiple Simulation Presentation (MSP) and…
Descriptors: Anxiety, Courseware, Probability, Computer Simulation
Pardos, Zachary A.; Baker, Ryan S. J. D.; San Pedro, Maria O. C. Z.; Gowda, Sujith M.; Gowda, Supreeth M. – Journal of Learning Analytics, 2014
In this paper, we investigate the correspondence between student affect and behavioural engagement in a web-based tutoring platform throughout the school year and learning outcomes at the end of the year on a high-stakes mathematics exam in a manner that is both longitudinal and fine-grained. Affect and behaviour detectors are used to estimate…
Descriptors: Affective Behavior, Student Behavior, Learner Engagement, Web Based Instruction
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