Hello everyone. This is Xue-Wang Geng, a D3 student.

I would like to introduce a paper I read for the English seminar. Below is a summary of the content written in the paper.

Paper Title: Adaptive or adapted to: Sequence and reflexive thematic analysis to understand learners’ self-regulated learning in an adaptive learning analytics dashboard
Journal: British Journal of Educational Technology
Pages: 1-28
Publication Year: 2022
Authors: Eunsung Park, Dirk Ifenthaler, Roy B. Clariana

With the development of ICT, the number of solutions supporting Self-Regulated Learning is increasing. Among them, Learning Analytics (LA) is one such solution. LA is defined as the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs (Siemens & Long, 2011). It has been reported that using LA enables:
・Support for controlling and monitoring learning situations
・Promotion of reflection and awareness of learning engagement
・Improvement of motivation, learning experience, and retention rates
and so on.

However, the authors point out that many LA studies focus on promoting learning performance and predictive models, and why using LA is effective has not been sufficiently investigated. Therefore, it is necessary to clarify how learners change behaviorally and strategically in Self-Regulated Learning through interventions utilizing LA. Furthermore, they argue that we should grasp how learning takes place by using LA from the learner’s perspective. In the paper I selected, the authors designed an Adaptive Learning Analytics Dashboard (ALAD) and focused on how learners start and conduct learning with the ALAD, investigating learners’ learning experiences and perceptions when interacting with the ALAD.

Regarding the ALAD in this study, a warm-up test (WU, prior knowledge test) was set at the beginning of each lesson. After learners take the WU test, the test scores are reflected in their individual ALAD. In the ALAD, the number of contents learners can access varies depending on whether they take the WU test and their test scores. Then, learners can check information from the ALAD such as their current learning status, time spent, and remaining time until completing each lesson. Also, the ALAD suggests the content that learners should study next.

To investigate how learners were learning in the ALAD, an experiment was conducted for one semester with 81 students taking a finance course. The course consisted of two face-to-face classes per week and online learning via the ALAD. During the implementation period, all interactions between learners and the ALAD were recorded. In addition, interviews were conducted with 12 learners at two time points.

As a result, three groups classified by cluster analysis regarding the use of the WU test were identified: the WU group that mainly used the WU test, the content group that focused on learning content, and the mixed group that balanced the WU test and content learning more evenly in terms of time. Then, as a result of sequence analysis, differences were observed in the learning sequences of the three groups. Learners in the WU group took the WU test at the beginning of each lesson and then studied the content, whereas the content group skipped the WU test and spent time studying the content. Furthermore, the results of the reflexive thematic analysis provided insights into how learners used the ALAD functions to construct learning strategies and how they monitored and controlled their learning. In particular, it is suggested that different ALAD usage arose from differences in awareness, such as whether the WU test is useful for achieving learning goals. For example, the WU group began to use the test as a means to achieve their goals (high scores) and constructed learning strategies to make the most of the test. In contrast, the content group recognized that the test was not useful for achieving their goals and employed different learning strategies, such as proceeding with learning content without taking the test. Note that this paper proposes several precautions for dashboard design.
・Recommendation functions should be devised and proposed by instructors or teachers in advance based on what learners need during their learning, rather than data-driven suggestions based on whether the test was correct or incorrect
・If tests can be taken multiple times, it is necessary to provide learners with additional and alternative resources for the test to avoid meaningless trial and error

In my opinion, this paper’s use of reflexive thematic analysis to qualitatively evaluate the impact of dashboard usage on learners’ learning behaviors and strategies was very helpful as an analytical method for the research I am currently working on. However, while this paper discussed how to support Self-Regulated Learning using a dashboard overall, I am curious about how the dashboard was designed to facilitate Self-Regulated Learning. Also, I think it would be more interesting if there were considerations based on specific learning behaviors. For example, I felt it was necessary to investigate what information learners check on the dashboard before proceeding to the next content learning or WU test. In my future research, I would like to explore the impact of dashboard functions on learning behaviors at a finer granularity.