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Marcel R. Haas; Colin Caprani; Benji T. van Beurden – Journal of Learning Analytics, 2023
We present an innovative modelling technique that simultaneously constrains student performance, course difficulty, and the sensitivity with which a course can differentiate between students by means of grades. Grade lists are the only necessary ingredient. Networks of courses will be constructed where the edges are populations of students that…
Descriptors: Bayesian Statistics, Computer Software, Learning Analytics, Grades (Scholastic)
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McEneaney, John; Morsink, Paul – Journal of Learning Analytics, 2022
Learning analytics (LA) provides tools to analyze historical data with the goal of better understanding how curricular structures and features have impacted student learning. Forward-looking curriculum design, however, frequently involves a degree of uncertainty. Historical data may be unavailable, a contemplated modification to curriculum may be…
Descriptors: Curriculum Design, Learning Analytics, Educational Change, Computer Software
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Valentina Albano; Donatella Firmani; Luigi Laura; Jerin George Mathew; Anna Lucia Paoletti; Irene Torrente – Journal of Learning Analytics, 2023
Multiple-choice questions (MCQs) are widely used in educational assessments and professional certification exams. Managing large repositories of MCQs, however, poses several challenges due to the high volume of questions and the need to maintain their quality and relevance over time. One of these challenges is the presence of questions that…
Descriptors: Natural Language Processing, Multiple Choice Tests, Test Items, Item Analysis
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Munch, Elizabeth – Journal of Learning Analytics, 2017
Topological data analysis (TDA) is a collection of powerful tools that can quantify shape and structure in data in order to answer questions from the data's domain. This is done by representing some aspect of the structure of the data in a simplified topological signature. In this article, we introduce two of the most commonly used topological…
Descriptors: Data Analysis, Topology, Graphs, Proximity
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Winne, Philip H.; Teng, Kenny; Chang, Daniel; Lin, Michael Pin-Chuan; Marzouk, Zahia; Nesbit, John C.; Patzak, Alexandra; Rakovic, Mladen; Samadi, Donya; Vytasek, Jovita – Journal of Learning Analytics, 2019
Data used in learning analytics rarely provide strong and clear signals about how learners process content. As a result, learning as a process is not clearly described for learners or for learning scientists. GaĊĦevic, Dawson, and Siemens (2015) urged data be sought that more straightforwardly describe processes in terms of events within learning…
Descriptors: Learning Analytics, Learning Processes, Independent Study, Computer Software
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Khosravi, Hassan; Kitto, Kirsty; Williams, Joseph Jay – Journal of Learning Analytics, 2019
This paper presents a platform called RiPPLE (Recommendation in Personalised Peer-Learning Environments) that recommends personalized learning activities to students based on their knowledge state from a pool of crowdsourced learning activities that are generated by educators and the students themselves. RiPPLE integrates insights from…
Descriptors: Data Analysis, Learning Activities, Management Systems, Foreign Countries
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Hadavand, Aboozar; Muschelli, John; Leek, Jeffrey – Journal of Learning Analytics, 2019
Due to the fundamental differences between traditional education and massive open online courses (MOOCs), and because of the ever-increasing popularity of the latter, more research is needed to understand current and future trends in MOOCs. Although research in the field has grown rapidly in recent years, one of the main challenges facing…
Descriptors: Learning Analytics, Student Behavior, Online Courses, Large Group Instruction