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Hanqiang Liu; Xiao Chen; Feng Zhao – Education and Information Technologies, 2024
Massive open online courses (MOOCs) have become one of the most popular ways of learning in recent years due to their flexibility and convenience. However, high dropout rate has become a prominent problem that hinders the further development of MOOCs. Therefore, the prediction of student dropouts is the key to further enhance the MOOCs platform.…
Descriptors: MOOCs, Video Technology, Behavior Patterns, Prediction
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)
Paassen, Benjamin; McBroom, Jessica; Jeffries, Bryn; Koprinska, Irena; Yacef, Kalina – Journal of Educational Data Mining, 2021
Educational data mining involves the application of data mining techniques to student activity. However, in the context of computer programming, many data mining techniques can not be applied because they require vector-shaped input, whereas computer programs have the form of syntax trees. In this paper, we present ast2vec, a neural network that…
Descriptors: Data Analysis, Programming Languages, Networks, Novices
Zhou, Yuhao; Li, Xihua; Cao, Yunbo; Zhao, Xuemin; Ye, Qing; Lv, Jiancheng – International Educational Data Mining Society, 2021
In educational applications, "Knowledge Tracing" (KT) has been widely studied for decades as it is considered a fundamental task towards adaptive online learning. Among proposed KT methods, Deep Knowledge Tracing (DKT) and its variants are by far the most effective ones due to the high flexibility of the neural network. However, DKT…
Descriptors: Online Courses, Computer Assisted Instruction, Networks, Learning Analytics
Mao, Ye; Shi, Yang; Marwan, Samiha; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2021
As students learn how to program, both their programming code and their understanding of it evolves over time. In this work, we present a general data-driven approach, named "Temporal-ASTNN" for modeling student learning progression in open-ended programming domains. Temporal-ASTNN combines a novel neural network model based on abstract…
Descriptors: Programming, Computer Science Education, Learning Processes, Learning Analytics
Zhongdi Wu; Eric Larson; Makoto Sano; Doris Baker; Nathan Gage; Akihito Kamata – Grantee Submission, 2023
In this investigation we propose new machine learning methods for automated scoring models that predict the vocabulary acquisition in science and social studies of second grade English language learners, based upon free-form spoken responses. We evaluate performance on an existing dataset and use transfer learning from a large pre-trained language…
Descriptors: Prediction, Vocabulary Development, English (Second Language), Second Language Learning

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