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Abbas, Mohsin; van Rosmalen, Peter; Kalz, Marco – IEEE Transactions on Learning Technologies, 2023
For predicting and improving the quality of essays, text analytic metrics (surface, syntactic, morphological, and semantic features) can be used to provide formative feedback to the students in higher education. In this study, the goal was to identify a sufficient number of features that exhibit a fair proxy of the scores given by the human raters…
Descriptors: Feedback (Response), Automation, Essays, Scoring
Wang, Qi; Rose, Carolyn P.; Ma, Ning; Jiang, Shiyan; Bao, Haogang; Li, Yanyan – IEEE Transactions on Learning Technologies, 2022
Forums are essential components facilitating interactions in online courses. However, in large-scale courses, many posts generated, which results in learners' difficulties. First, the posts are poorly organized and some deviate from the topic, making it difficult for learners' knowledge acquisition. Second, learners cannot receive timely feedback…
Descriptors: Design, Automation, Feedback (Response), Scaffolding (Teaching Technique)
Qiao, Chen; Hu, Xiao – IEEE Transactions on Learning Technologies, 2023
Free text answers to short questions can reflect students' mastery of concepts and their relationships relevant to learning objectives. However, automating the assessment of free text answers has been challenging due to the complexity of natural language. Existing studies often predict the scores of free text answers in a "black box"…
Descriptors: Computer Assisted Testing, Automation, Test Items, Semantics
Ricardo Conejo Muñoz; Beatriz Barros Blanco; José del Campo-Ávila; José L. Triviño Rodriguez – IEEE Transactions on Learning Technologies, 2024
Automatic question generation and the assessment of procedural knowledge is still a challenging research topic. This article focuses on the case of it, the techniques of parsing grammars for compiler construction. There are two well-known techniques for parsing: top-down parsing with LL(1) and bottom-up with LR(1). Learning these techniques and…
Descriptors: Automation, Questioning Techniques, Knowledge Level, Language
Bei Cai; Ziyu He; Hong Fu; Yang Zheng; Yanjie Song – IEEE Transactions on Learning Technologies, 2025
Much research has applied automated writing evaluation (AWE) systems to English writing instruction; however, understanding how students internalize and apply this feedback to reduce writing errors is difficult, largely due to the personal and private nature of this process. Therefore, this research utilized eye-tracking technology to explore the…
Descriptors: Undergraduate Students, Majors (Students), Writing (Composition), Writing Evaluation
Dominguez, Federico; Ochoa, Xavier; Zambrano, Dick; Camacho, Katherine; Castells, Jaime – IEEE Transactions on Learning Technologies, 2021
Multimodal learning analytics, which is collection, analysis, and report of diverse learning traces to better understand and improve the learning process, has been producing a series of interesting prototypes to analyze learning activities that were previously hard to objectively evaluate. However, none of these prototypes have been taken out of…
Descriptors: Learning Modalities, Learning Analytics, Automation, Oral Language
Liu, Ming; Li, Yi; Xu, Weiwei; Liu, Li – IEEE Transactions on Learning Technologies, 2017
Writing an essay is a very important skill for students to master, but a difficult task for them to overcome. It is particularly true for English as Second Language (ESL) students in China. It would be very useful if students could receive timely and effective feedback about their writing. Automatic essay feedback generation is a challenging task,…
Descriptors: Foreign Countries, College Students, Second Language Learning, English (Second Language)