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Yingbin Zhang; Yafei Ye; Luc Paquette; Yibo Wang; Xiaoyong Hu – Journal of Computer Assisted Learning, 2024
Background: Learning analytics (LA) research often aggregates learning process data to extract measurements indicating constructs of interest. However, the warranty that such aggregation will produce reliable measurements has not been explicitly examined. The reliability evidence of aggregate measurements has rarely been reported, leaving an…
Descriptors: Learning Analytics, Learning Processes, Test Reliability, Psychometrics
Stanja, Judith; Gritz, Wolfgang; Krugel, Johannes; Hoppe, Anett; Dannemann, Sarah – British Journal of Educational Technology, 2023
Formative assessment is considered to be helpful in students' learning support and teaching design. Following Aufschnaiter's and Alonzo's framework, formative assessment practices of teachers can be subdivided into three practices: eliciting evidence, interpreting evidence and responding. Since students' conceptions are judged to be important for…
Descriptors: Formative Evaluation, Student Attitudes, Learning Analytics, Student Evaluation
Xu, Weiqi; Wu, Yajuan; Ouyang, Fan – International Journal of Educational Technology in Higher Education, 2023
Pair programming (PP), as a mode of collaborative problem solving (CPS) in computer programming education, asks two students work in a pair to co-construct knowledge and solve problems. Considering the complex multimodality of pair programming caused by students' discourses, behaviors, and socio-emotions, it is of critical importance to examine…
Descriptors: Cooperative Learning, Problem Solving, Computer Science Education, Programming
Liu, Zhichun; Moon, Jewoong – Educational Technology & Society, 2023
In this study, we have proposed and implemented a sequential data analytics (SDA)-driven methodological framework to design adaptivity for digital game-based learning (DGBL). The goal of this framework is to facilitate children's personalized learning experiences for K-5 computing education. Although DGBL experiences can be beneficial, young…
Descriptors: Learning Analytics, Design, Game Based Learning, Computation
Amaya, Edna Johanna Chaparro; Restrepo-Calle, Felipe; Ramírez-Echeverry, Jhon J. – Journal of Information Technology Education: Research, 2023
Aim/Purpose: This article proposes a framework based on a sequential explanatory mixed-methods design in the learning analytics domain to enhance the models used to support the success of the learning process and the learner. The framework consists of three main phases: (1) quantitative data analysis; (2) qualitative data analysis; and (3)…
Descriptors: Learning Analytics, Guidelines, Student Attitudes, Learning Processes
Arjan J. F. Kok; Lex Bijlsma; Cornelis Huizing; Ruurd Kuiper; Harrie Passier – Informatics in Education, 2024
This paper presents the first experiences of the use of an online open-source repository with programming exercises. The repository is independent of any specific teaching approach. Students can search for and select an exercise that trains the programming concepts that they want to train and that only uses the programming concepts they already…
Descriptors: Programming Languages, Computer Science Education, Open Source Technology, Teaching Methods
Xu, Jia; Wei, Tingting; Lv, Pin – International Educational Data Mining Society, 2022
In an Intelligent Tutoring System (ITS), problem (or question) difficulty is one of the most critical parameters, directly impacting problem design, test paper organization, result analysis, and even the fairness guarantee. However, it is very difficult to evaluate the problem difficulty by organized pre-tests or by expertise, because these…
Descriptors: Prediction, Programming, Natural Language Processing, Databases
Ait-Adda, Samia; Bousbia, Nabila; Balla, Amar – E-Learning and Digital Media, 2023
Our aim in this paper is to improve the efficiency of a learning process by using learners' traces to detect particular needs. The analysis of the semantic path of a learner or group of learners during the learning process can allow detecting those students who are in needs of help as well as identify the insufficiently mastered concepts. We…
Descriptors: Semantics, Learning Processes, Learning Analytics, Models
Aguilar, J.; Buendia, O.; Pinto, A.; Gutiérrez, J. – Interactive Learning Environments, 2022
Social Learning Analytics (SLA) seeks to obtain hidden information in large amounts of data, usually of an educational nature. SLA focuses mainly on the analysis of social networks (Social Network Analysis, SNA) and the Web, to discover patterns of interaction and behavior of educational social actors. This paper incorporates the SLA in a smart…
Descriptors: Learning Analytics, Cognitive Style, Socialization, Social Networks
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
Tlili, Ahmed; Denden, Mouna; Essalmi, Fathi; Jemni, Mohamed; Chang, Maiga; Kinshuk; Chen, Nian-Shing – Interactive Learning Environments, 2023
The ability of automatically modeling learners' personalities is an important step in building adaptive learning environments. Several studies showed that knowing the personality of each learner can make the learning interaction with the provided learning contents and activities within learning systems more effective. However, the traditional…
Descriptors: Learning Analytics, Learning Management Systems, Intelligent Tutoring Systems, Bayesian Statistics
Saito, Daisuke; Kaieda, Shota; Washizaki, Hironori; Fukazawa, Yoshiaki – Journal of Information Technology Education: Innovations in Practice, 2020
Aim/Purpose: Although many computer science measures have been proposed, visualizing individual students' capabilities is difficult, as those measures often rely on specific tools and methods or are not graded. To solve these problems, we propose a rubric for measuring and visualizing the effects of learning computer programming for elementary…
Descriptors: Scoring Rubrics, Visualization, Learning Analytics, Computer Science Education
Hundhausen, C. D.; Olivares, D. M.; Carter, A. S. – ACM Transactions on Computing Education, 2017
In recent years, learning process data have become increasingly easy to collect through computer-based learning environments. This has led to increased interest in the field of "learning analytics," which is concerned with leveraging learning process data in order to better understand, and ultimately to improve, teaching and learning. In…
Descriptors: Learning Analytics, Computer Science Education, Programming, Learning Processes