This study investigates the effects of student use of self-regulated learning (SRL) strategies on their computer programming achievement. Ninety-six students from undergraduate teacher training programmes offered by a Hong Kong university voluntarily participated in the study. Sixty-six of them were first-year students enrolling on an introductory Java programming course, while 30 were second-year students enrolling on an advanced Java programming course. The SRL strategies adopted by participants were measured by the Motivated Strategies for Learning Questionnaire (MSLQ) and were exemplified from the reflective writing of their electronic portfolios. Their achievement in computer programming was evaluated using continuous and end of course assessments. The findings of this study suggest that higher-order cognitive strategies (i.e. elaboration, organization, critical thinking), metacognitive control strategies (i.e. self-regulation) and resource management strategies (i.e. time and study environment management, help seeking) are likely to facilitate a prolonged achievement of computer programming for both novices and non-novices. They can provide insights into designing adequate SRL strategy training to support student learning in computer programming. Copyright © 2021 SCHOLINK INC.

Previous research suggested that bring your own device (BYOD) initiatives can potentially promote student engagement in learning and lead to improvements in learning achievement. However, there has been limited studies looking at how to design and implement BYOD to realise its full potential, especially in blended classroom settings. The present study was designed to fill this gap by adapting the community of inquiry (CoI) framework to support and analyse the implementation of BYOD for blended English as Foreign Language (EFL) learning in a Hong Kong university. Findings from lesson videos, course assessments, focus group interviews and student reflection are reported and discussed. This study can contribute to understanding how BYOD could benefit student learning through affording different types of presence in the CoI framework. It can also provide insights into possible ways of using BYOD to promote meaningful learning in the blended classroom. Copyright © 2022 Elsevier Inc. All rights reserved.

This paper describes ongoing research on teachers’ concerns about STEM education in Hong Kong. The work is being conducted under the auspices of a government-funded project. A large-scale, regionally representative sample of primary and secondary school teachers in Hong Kong will be selected, and their key concerns about STEM education will be surveyed. To the best of our knowledge, the study is the first to examine teachers’ concerns about STEM education in Hong Kong. The findings of the study will make significant and original contributions to policymakers, teachers, and researchers. Copyright © 2020 by the Association for the Advancement of Computing in Education (AACE).

BACKGROUND: Research on Artificial Intelligence in Education (AIEd) has become increasingly active during the past 20 years, with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in the application scope of AIEd research. A comprehensive overview of this field is necessary to understand what had been concerned in AIEd research, what is going on in the field of AIEd, and what might be the future of AIEd research. METHODS: This paper combined the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale AIEd literature in the past two decades. Specifically, by using STM, we uncovered important research topics concerned within the AIEd community. The evolution of these topics was explored by using a nonparametric Mann-Kendall (MK) trend test. In addition, the correlation between these topics was further explored and visualized based on a semi-parametric Gaussian procedure. RESULTS: The annual trend of publications concerning AIEd had grown consistently across the past 20 years, indicating that research on AIEd had received a growing interest from academia Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics and promising research orientations. Specifically, the top six topics having the highest proportions in the dataset were cognition and perception, programming education, prediction, MOOCs, semantic Web and recommender systems, assessment and feedback, and emotion detection. Results of the MK test highlighted five topics receiving an increase in research interest, including prediction, assessment and feedback, flipped classroom and biosignal data, STEM education, and English language learning. In addition, several inter-topic research directions were identified, including 1) flipped classroom and biosignal data, emotion detection, and

Teacher feedback can be useful in helping English as Foreign Language (EFL) students revise their draft writing. Investigating how EFL students respond to various types of teacher feedback in draft revision has been regarded as an important field of study. However, such investigation is time-consuming and labour-intensive, which limits its practical application in EFL writing classes. To address this issue, this study was designed to explore an initiative to use a systematic method in tracking and analysing student revisions in response to teacher feedback. Firstly, an automated tracking method was proposed and applied to examine the technical feasibility of analysing the effects of teacher feedback on student revisions for 75 EFL undergraduate students from two Hong Kong universities. Secondly, the study explicitly sought to explore teachers’ perspectives on the automated tracking method by interviews. The quantitative results suggest that the automated tracking method can analyse teacher feedback and student revision with high accuracy compared to the manual analysis, while the qualitative results shed light on the benefits and limitations of the method. Overall, our findings provide a basis for further research on harnessing the potential of technology to support teachers in developing their professional feedback practice for EFL writing. Copyright © 2021 Informa UK Limited, trading as Taylor & Francis Group.

This paper aims to report and discuss the findings of a research project examining the effects of students‘ use of self-regulated learning (SRL) strategies on their computer programming achievement in higher education. In the project, data were collected from 66 undergraduate students who enrolled in a 13-week introductory computer programming course offered by a Hong Kong university during the academic year 2018/19. Participants were asked to complete the Motivated Strategies for Learning Questionnaire (MSLQ) to measure their use of SRL strategies. They were also asked to write weekly reflective journals to reflect on their learning progress and plan their future actions for improvement. Their programming achievement was assessed by individual exercises and an end-of-course examination as part of the course assessment. The project adopted a mixed method approach to data analysis. A quantitative correlational analysis on the questionnaire and assessment scores was conducted to explore the relationship between SRL strategies and programming achievement. In addition, a qualitative content analysis on the reflective journals was carried out to identify differences in programming problems and SRL strategies used to tackle the problems between high- and low-achieving students. The findings of the project indicate that higher-order cognitive strategies, metacognitive control strategies and collaborative learning strategies were positively associated with programming achievement. The findings also suggest that reflective journaling can be a good way for students to reflect on their programming performance and think about strategies for improvement. Copyright © 2019 ICEDU.

This study explored the demographic predictors of teachers’ stages of concern (SoC) for science, technology, engineering, and mathematics (STEM) education in Hong Kong. Results of multiple regression analysis demonstrated that teaching sector and participation in STEM education training predicted four SoC, whereas teaching subject area predicted two SoC. We discussed the significance of these findings by adopting a personalized approach to teacher professional learning. Copyright © ICCE 2020 - 28th International Conference on Computers in Education, Proceedings. All rights reserved.

This study was designed to explore how secondary students perceive the use of flipped classroom for learning computer programming. Specifically, it aimed to investigate the effects of flipped classroom on students’ learning and acquisition of programming knowledge and skills. Flipped classroom is known as a blended learning approach in which learning materials are delivered online, out of class for self-study in advance of classes, while homework assignments are transformed into class activities. This approach leverages technology and digital resources to support students in independent and online learning. It also enables teachers to minimize direct instruction and maximize student involvement in both teacher-student and student-student interactions. In this study, forty students from two Information and Communications Technology (ICT) classes (18 students in Secondary 4 and 22 in Secondary 5) in a Hong Kong secondary school were involved. Flipped classroom was adopted to teach students about computer programming topics (conditional, repetition and array) in both classes. Data were collected from a questionnaire and two interview sessions to explore students’ views on using flipped classroom to learn computer programming. The findings of this study indicate that students in the flipped classroom, regardless of their performance level, experienced stronger grasp of programming knowledge and higher engagement in learning programming concepts. Our findings also highlight limitations with this study that could be addressed in future work. Copyright © 2019 Springer Nature Singapore Pte Ltd.

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.

This paper reports and discusses the findings from a study exploring the use of Bring Your Own Device (BYOD) for teaching and learning at a university in Hong Kong. Seventy-one undergraduate students enrolled on an English course participated in the study. A pedagogical framework is proposed to guide the design of BYOD-mediated lessons, and the potential of BYOD pedagogies to support teaching and learning is examined. Learning gains from BYOD-mediated lessons are reflected in students’ assessment results. Some merits and challenges associated with BYOD-mediated learning are emerged from students’ focus group responses. The findings contribute to an expanded understanding of the BYOD experience across educational settings.