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Xinyu Li; Yizhou Fan; Tongguang Li; Mladen Rakovic; Shaveen Singh; Joep van der Graaf; Lyn Lim; Johanna Moore; Inge Molenaar; Maria Bannert; Dragan Gaševic – Journal of Learning Analytics, 2025
The focus of education is increasingly on learners' ability to regulate their own learning within technology-enhanced learning environments. Prior research has shown that self-regulated learning (SRL) leads to better learning performance. However, many learners struggle to productively self-regulate their learning, as they typically need to…
Descriptors: Learning Analytics, Metacognition, Independent Study, Skill Development
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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. Gaševic, 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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McHugh, Douglas; Feinn, Richard; McIlvenna, Jeff; Trevithick, Matt – Education Sciences, 2021
Learner-centered coaching and feedback are relevant to various educational contexts. Spaced retrieval enhances long-term knowledge retention. We examined the efficacy of Blank Slate, a novel spaced retrieval software application, to promote learning and prevent forgetting, while gathering and analyzing data in the background about learners'…
Descriptors: Randomized Controlled Trials, Learning Analytics, Coaching (Performance), Formative Evaluation
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Sanguino, Juan; Manrique, Rubén; Mariño, Olga; Linares-Vásquez, Mario; Cardozo, Nicolas – International Educational Data Mining Society, 2022
Recommender systems in educational contexts have proven effective to identify learning resources that fit the interests and needs of learners. Their usage has been of special interest in online self-learning scenarios to increase student retention and improve the learning experience. In current recommendation techniques, and in particular, in…
Descriptors: Data Analysis, Learning Analytics, Student Interests, Student Needs
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Viberg, Olga; Kukulska-Hulme, Agnes; Peeters, Ward – International Journal of Mobile and Blended Learning, 2023
Mobile-assisted language learning (MALL) research includes examination and development of second language learners' cognitive and metacognitive self-regulated learning skills, but the affective learning component of self-regulation in this context remains largely unexplored. Support for affective learning, which is defined by learners' beliefs,…
Descriptors: Metacognition, Computer Assisted Instruction, Second Language Learning, Second Language Instruction
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Andy Ding-Xuan Ng; Aloysius Ong; Alwyn Vwen Yen Lee; Chew Lee Teo – Pedagogies: An International Journal, 2024
Research and development of Learning Analytics (LA) have created new ways to support students' learning. However, our understanding of teachers' roles when implementing LA in classroom practices remains nascent. This study investigates how teachers can implement LA to support students' agency in directing their own inquiry, when engaging in a…
Descriptors: Learning Analytics, Grade 5, Elementary School Students, Grade 6
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Zhongzhou Chen; Tom Zhang; Michelle Taub – Journal of Learning Analytics, 2024
The current study measures the extent to which students' self-regulated learning tactics and learning outcomes change as the result of a deliberate, data-driven improvement in the learning design of mastery-based online learning modules. In the original design, students were required to attempt the assessment once before being allowed to access…
Descriptors: Learning Analytics, Algorithms, Instructional Materials, Course Content
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Kim, Byungsoo; Yu, Hangyeol; Shin, Dongmin; Choi, Youngduck – International Educational Data Mining Society, 2021
The needs for precisely estimating a student's academic performance have been emphasized with an increasing amount of attention paid to Intelligent Tutoring System (ITS). However, since labels for academic performance, such as test scores, are collected from outside of ITS, obtaining the labels is costly, leading to label-scarcity problem which…
Descriptors: Academic Achievement, Intelligent Tutoring Systems, Prediction, Scores