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Wollny, Sebastian; Di Mitri, Daniele; Jivet, Ioana; Muñoz-Merino, Pedro; Scheffel, Maren; Schneider, Jan; Tsai, Yi-Shan; Whitelock-Wainwright, Alexander; Gaševic, Dragan; Drachsler, Hendrik – Journal of Computer Assisted Learning, 2023
Background: Learning Analytics (LA) is an emerging field concerned with measuring, collecting, and analysing data about learners and their contexts to gain insights into learning processes. As the technology of Learning Analytics is evolving, many systems are being implemented. In this context, it is essential to understand stakeholders'…
Descriptors: Foreign Countries, College Students, Learning Analytics, Expectation
Fan, Yizhou; Tan, Yuanru; Rakovic, Mladen; Wang, Yeyu; Cai, Zhiqiang; Shaffer, David Williamson; Gaševic, Dragan – Journal of Computer Assisted Learning, 2023
Background: Select and enact appropriate learning tactics that advance learning has been considered a critical set of skills to successfully complete highly flexible online courses, such as Massive open online courses (MOOCs). However, limited by analytic methods that have been used in the past, such as frequency distribution, sequence mining and…
Descriptors: MOOCs, Students, Learning Processes, Learning Strategies
Yan, Lixiang; Martinez-Maldonado, Roberto; Zhao, Linxuan; Dix, Samantha; Jaggard, Hollie; Wotherspoon, Rosie; Li, Xinyu; Gaševic, Dragan – British Journal of Educational Technology, 2023
Simulation-based learning provides students with unique opportunities to develop key procedural and teamwork skills in close-to-authentic physical learning and training environments. Yet, assessing students' performance in such situations can be challenging and mentally exhausting for teachers. Multimodal learning analytics can support the…
Descriptors: Learning Analytics, Simulation, Teamwork, Cooperative Learning
Darvishi, Ali; Khosravi, Hassan; Sadiq, Shazia; Gaševic, Dragan – British Journal of Educational Technology, 2022
Peer assessment has been recognised as a sustainable and scalable assessment method that promotes higher-order learning and provides students with fast and detailed feedback on their work. Despite these benefits, some common concerns and criticisms are associated with the use of peer assessments (eg, scarcity of high-quality feedback from peer…
Descriptors: Artificial Intelligence, Learning Analytics, Peer Evaluation, Student Evaluation
Mangaroska, Katerina; Martinez-Maldonado, Roberto; Vesin, Boban; Gaševic, Dragan – Journal of Computer Assisted Learning, 2021
Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA…
Descriptors: Computer Science Education, Student Attitudes, Learning Modalities, Learning Analytics
Weidlich, Joshua; Gaševic, Dragan; Drachsler, Hendrik – Journal of Learning Analytics, 2022
As a research field geared toward understanding and improving learning, Learning Analytics (LA) must be able to provide empirical support for causal claims. However, as a highly applied field, tightly controlled randomized experiments are not always feasible nor desirable. Instead, researchers often rely on observational data, based on which they…
Descriptors: Causal Models, Inferences, Learning Analytics, Comparative Analysis
Tsai, Yi-Shan; Poquet, Oleksandra; Gaševic, Dragan; Dawson, Shane; Pardo, Abelardo – British Journal of Educational Technology, 2019
Learning analytics (LA) has demonstrated great potential in improving teaching quality, learning experience and administrative efficiency. However, the adoption of LA in higher education is often beset by challenges in areas such as resources, stakeholder buy-in, ethics and privacy. Addressing these challenges in a complex system requires agile…
Descriptors: Learning Analytics, Higher Education, Leadership, Educational Innovation
Er, Erkan; Dimitriadis, Yannis; Gaševic, Dragan – Assessment & Evaluation in Higher Education, 2021
Although dialogue can augment the impact of feedback on student learning, dialogic feedback is unaffordable by instructors teaching large classes. In this regard, peer feedback can offer a scalable and effective solution. However, the existing practices optimistically rely on students' discussion about feedback and lack a systematic design…
Descriptors: Cooperative Learning, Peer Evaluation, Feedback (Response), Learning Analytics
Whitelock-Wainwright, Alexander; Gaševic, Dragan; Tsai, Yi-Shan; Drachsler, Hendrik; Scheffel, Maren; Muñoz-Merino, Pedro J.; Tammets, Kairit; Delgado Kloos, Carlos – Journal of Computer Assisted Learning, 2020
To assist higher education institutions in meeting the challenge of limited student engagement in the implementation of Learning Analytics services, the Questionnaire for Student Expectations of Learning Analytics (SELAQ) was developed. This instrument contains 12 items, which are explained by a purported two-factor structure of "Ethical and…
Descriptors: Questionnaires, Test Construction, Test Validity, Learning Analytics
Fan, Yizhou; Matcha, Wannisa; Uzir, Nora'ayu Ahmad; Wang, Qiong; Gaševic, Dragan – International Journal of Artificial Intelligence in Education, 2021
The importance of learning design in education is widely acknowledged in the literature. Should learners make effective use of opportunities provided in a learning design, especially in online environments, previous studies have shown that they need to have strong skills for self-regulated learning (SRL). The literature, which reports the use of…
Descriptors: Learning Analytics, Instructional Design, Independent Study, Multivariate Analysis
Whitelock-Wainwright, Alexander; Gaševic, Dragan; Tejeiro, Ricardo; Tsai, Yi-Shan; Bennett, Kate – Journal of Computer Assisted Learning, 2019
Student engagement within the development of learning analytics services in Higher Education is an important challenge to address. Despite calls for greater inclusion of stakeholders, there still remains only a small number of investigations into students' beliefs and expectations towards learning analytics services. Therefore, this paper presents…
Descriptors: Expectation, Learning Analytics, Questionnaires, College Students
Martinez-Maldonado, Roberto; Gaševic, Dragan; Echeverria, Vanessa; Fernandez Nieto, Gloria; Swiecki, Zachari; Buckingham Shum, Simon – Journal of Learning Analytics, 2021
Using data to generate a deeper understanding of collaborative learning is not new, but automatically analyzing log data has enabled new means of identifying key indicators of effective collaboration and teamwork that can be used to predict outcomes and personalize feedback. Collaboration analytics is emerging as a new term to refer to…
Descriptors: Learning Analytics, Cooperative Learning, Validity, Case Studies
Mangaroska, Katerina; Sharma, Kshitij; Gaševic, Dragan; Giannakos, Michail – Journal of Computer Assisted Learning, 2022
Background: Problem-solving is a multidimensional and dynamic process that requires and interlinks cognitive, metacognitive, and affective dimensions of learning. However, current approaches practiced in computing education research (CER) are not sufficient to capture information beyond the basic programming process data (i.e., IDE-log data).…
Descriptors: Cognitive Processes, Psychological Patterns, Problem Solving, Programming
Sher, Varshita; Hatala, Marek; Gaševic, Dragan – Journal of Learning Analytics, 2022
Recent advances in smart devices and online technologies have facilitated the emergence of ubiquitous learning environments for participating in different learning activities. This poses an interesting question about modality access, i.e., what students are using each platform for and at what time of day. In this paper, we present a log-based…
Descriptors: Time Factors (Learning), Use Studies, Learning Management Systems, Handheld Devices
Ahmad Uzir, Nora'ayu; Gaševic, Dragan; Matcha, Wannisa; Jovanovic, Jelena; Pardo, Abelardo – Journal of Computer Assisted Learning, 2020
This paper aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. Specifically, an exploratory study was conducted on the dataset collected in three consecutive offerings of an undergraduate computer engineering course (N = 1,134). Trace data about activities were initially coded…
Descriptors: Time Management, Blended Learning, Learning Analytics, Undergraduate Students
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