Abstract
It is quite complex to adapt instruction to student needs in view of online education owing to the ensuing communication disconnection in such learning environments. Decision support schemes offer assistance by automatically gathering students’ data and forwarding them to the tutor in the appropriate perspective, in order to predict their behaviour and implement some action beforehand to avert or promote the final upshot. This study shows of a decision support scheme known as u-Tutor that is centred on the similarity computation between learners in the past, and how it was used in a real-case scenario. For this case study, this tool has been utilized by two real courses comprising of 392 learners alongside academic faculty, as of 2015 to 2019. The analysis offered focuses on 3 research areas: (1) perceived usefulness, (2) usability of the tool and (3) success rate of classification. From the acquired data, it can be seen that the teaching group managed to offer excellent approximations for those learners who eventually managed to pass the course, whereas u-Tutor seemed to be an early warning for learners at risk, indicating its capacity as a tutors’ supportive tool.
Highlights
Alleged expediency and usability are issues influencing the integration of any technological invention, thereby being considered intricate and significant topics being examined in learning analytics systems [15]
The case study discussed was overseen by three research areas that were depicted in five research questions
The following sub-sections show the results related to the three research areas: (1) perceived usefulness, (2) usability and (3) success rate of classification
Summary
Methods of data analysis are presently receiving attention from educational research literature as a study field. Learning analytics methods may be utilized in numerous diverse educational environments, like distance, face-to-face or blended. Distance learning is highly relevant in the learning analytics area based on two key reasons: to begin with, in online settings, both tutors and learners take part in a virtual learning environment (VLE) as their major interaction point. As a result, it is simpler, compared to blended or classroom environments, to capture the activity of most of the participants in the course [3]. Consistent with Vieira et al, [2] learning analytics methods may be used for numerous diverse purposes: comprehending teamwork, instructional design, comprehending motivation, enhancing reflection, or examining usage behaviors, among others
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