Blended learning pedagogical practices supported by learning management systems have become an important part of higher education curricula. In most cases, these blended curricula are evaluated through multimodal formative assessments. Although assessments can strongly affect student outcomes, research on the topic is limited. In this paper, we adopted a learning analytics approach to explore student engagement with formative assessments and the power of these assessments to predict student outcomes in an English for Academic Purposes courses in a Hong Kong university. The study retrieved the data logs from 7,815 students and used the data to analyze student engagement with the formative assessments. The results suggested that the students put effort into completing the assessments. The degree to which assessments predict learning outcomes depend on students’ level of subject knowledge and their understanding of the relevance of the assessments. This study showed that learning analytics provided reliable evidence for understanding students’ engagement and identifying at-risk students. Therefore, learning analytics research has the potential to inform pedagogical practice. Copyright © 2022 The Author(s).

In recent years, research using learning analytics to predict learning outcomes has begun to increase. This emerging field of research advocates the use of readily-available data to inform teaching and learning. The current case study adopts a learning analytics approach to evaluate the online learning package of an academic English course in a university in Hong Kong. This study aims to (1) explore the completion pattern of use of the online learning package by students in a generic undergraduate academic skills course; and (2) predict student outcomes based on their online behaviour patterns. Over three academic years, the study examined usage logs for 7000+ students that were available on the university's learning management system. Student assessment component scores, online activity completion rates, and online behavioural patterns were identified and examined using descriptive analysis, bivariate correlation analysis, and multiple regression analysis. The findings reveal insights into different online learning behavioural patterns that would benefit blended course designers. For instance, some students started using the online learning package early in the semester but fulfilled only the minimum required online work, whereas others greatly exceeded the basic requirement and continued doing activities in the online package even after the semester had finished. The relationship between learning activities in the online package and assessment component grades was found to be weak but meaningful. A regression model was developed drawing on the completion rates to predict overall student scores, and this model successfully identified several specific factors, such as total number of attempts and performance in individual online learning activities, as predictors of the final course grade.
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This paper reports and discusses the results of a study aimed at automatically categorising teacher feedback on student writing. A total of 3412 teachers’ written comments on 90 students’ draft essays were collected from an EFL course offered by a Hong Kong university during the first semester of 2016/17. The data were primarily used to design and implement an automated tool to classify teachers’ comments with respect to a taxonomy of their characteristics. The findings of this study show that the performance of the automated tool is comparable to that of human annotators, suggesting the feasibility of using the automatic approach to identify and analyse different types of teacher feedback. This study can contribute to future research into the investigation of the impact of teacher feedback on student writing in a big data world. Copyright © 2018 International Journal of Information and Education Technology. All rights reserved.