The presented study is the result of the research project ‘Open Online Learning for Digital and Networked Society (3.3-LMT-K-712-01-0189)’, funded by the European Social Fund according to the activity ‘Improvement of researchers’ qualification by implementing world-class R&D projects’ of Measure No. 09.3.3-LMT-K-712 under the grant agreement with the Research Council of Lithuania (LMTLT).
The research was focussed on the question how university teachers use learning analytics to engage learners in online learning courses.
In the study authors revealed how learning analytics data may inform and improve open and online learning by raising teachers’ awareness of students’ behaviour and performance, relevance of teaching and learning materials, methods used and other. An in-depth theoretical analysis as well as empirical research were conducted to achieve the goal of this research – to create the model of application of learning analytics as a metacognitive tool to enhance student success. The model focuses on teacher as an agent who is active in the process, planning and designing metacognitive strategies for students during the Learning Design Phase, collecting and following the data generated by learning analytics during Teaching and Learning Phase, implementing metacognition using learning analytics for teacher inquiry cycle during Teacher Metacognition Phase, and, finally, making decisions on learning design, as well as teaching and learning improvement during Regulation and Improvement Phase. Each phase is supported with a self-check statement block, allowing teachers to implement self-assessment if all the main objectives of each phase are properly taken into consideration in a given phase.
To be more specific, Learning Design Phase indicates five main objectives for the teacher who wants to create metacognitive strategies for students in the course. As theoretical insights reveal, student apprehension measurement should focus on teaching and learning processes, agents’ roles, awareness of learning design, academic success measurement, as well as planning student–teacher interventions, feedback and the tools that support teachers in monitoring and measuring progress and generating data, as well as evidence. The second phase – the Teaching and Learning Phase demonstrates and reminds teachers that if learning design solutions are chosen properly, then learning analytics will generate data during this phase about students’ consciousness of their roles, academic success, self-concept, and even learning design. In addition, learning analytics will generate data about student behaviour patterns, as well as teacher–student interventions and feedback. Student academic success data received from assessment of learning outcomes and compared with the other learning behaviour data in VLMs are seen as the most important components in this phase and success factors in preparation for the next one. Finally, Regulation and Improvement Phase reveal that teachers are expected to make short-term and long-term decisions, but also to think over and clarify their deeper understanding about learning. Self-assessment or self-check statements suggest teachers to test new scenarios and approaches, as well as to take into consideration student reflection and feedback.
This research confirmed that university teachers, despite having awareness of the possibility to access learning analytics data and use it for learning design and learners’ engagement, have very limited understanding of how this data could help them to make changes in learning design. However, the research also proved that teachers were able to understand learner engagement based on learning analytics data and they considered the data and act upon this data when they developed learner-centred activities and made changes in the course agenda. In terms of developing learner-centred activities, research disclosed that the use of learning analytics to facilitate learner engagement was helpful to facilitate teacher interventions to provoke learner critical thinking, to induce focused discussions, to stimulate learner’s personal interest and original understanding, and to raise learner’s awareness of the learning process. From the other side, the research indicated that the use of learning analytics data-driven information can foster changes in the course curriculum and/or learning design, by encouraging teachers to experiment with diverse student – centred learning activities, apply new learning and teaching approaches, and make sense of students’ reflections on the course tasks.
The fact that research participants had different online teaching experiences might have restricted research participants’ understanding of the use of learning analytics data for learning design improvement and students’ engagement. Despite the limitations mentioned, this study discloses the need for further in-depth research of teacher practices aiming to analyse how decisions are made based on specific problems, how the tools and online teaching methods help to ensure and stimulate social presence. This leads to the idea that a longitudinal study could provide a more detailed picture of how course curriculum is planned, revised, and changed based on learning analytics data, considering social, teaching, and cognitive presences.
Authors: Prof. Dr. Airina Volungevičienė, Prof. Habil. Dr. Margarita Teresevičienė, Assoc. Prof. Dr. Elena Trepulė, Vytautas Magnus University, Lithuania
To read the book, please click here.
We invite you also to get acquainted with the interview with the book authors that can be found here.
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