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Dr. Gary Cheng is currently the Acting Head and Associate Professor of the Department of Mathematics and Information Technology at The Education University of Hong Kong (EdUHK). Dr. Cheng's academic background is in computer science, but he has specialised in teaching Information Technology in education for over a decade. With substantial years of work experience in Hong Kong academia, Dr. Cheng has built a wealth of knowledge and a network of support to unleash the potential of technology for teacher education. He has a proven track record of exploring and evaluating the use of emerging technologies to enhance teaching and learning. Over the years, Dr. Cheng has been involved in research projects funded by EdUHK and the Research Grant Council of Hong Kong on a range of topics mainly related to technology enhanced learning.
Since Sundqvist introduced the term “extramural English” in 2009, empirical research on extramural language learning has continued to expand. However, the expanding empirical research has yet yielded incommensurate review studies. To present a timely picture of the field of extramural language learning, this study conducts a review of 33 relevant articles retrieved from Scopus and Web of Science databases. The results showed the five types of target languages frequently investigated in this field (i.e., English, German, French, Chinese, and Japanese) and seven main types of extramural learning activities (i.e., playing digital games, watching videos, reading, listening to audios, having technology-enhanced socialisation, having face-to-face socialisation, and writing compositions). People’s engagement in extramural language learning was overall high, especially listening to audios and playing digital games, mediated by the relationship between the difficulty of the activities and people’s target language proficiency levels, gender, and the interactive environment. Extramural language learning was overall effective for language development and enhancing affective states in language learning. The effectiveness may be influenced by the involvement of language inputs and outputs and the amount of engagement time. Implications for practitioners were suggested concerning encouraging digital gameplay, emphasising formal language instruction, and creating positive interactive environments for extramural language learning.
Ruofei Zhang; Di Zou; Gary Cheng; Haoran Xie; Fu Lee Wang; Oliver Tat Sheung Au. Target languages, types of activities, engagement, and effectiveness of extramural language learning. PLOS ONE 2021, 16, e0253431 .
AMA StyleRuofei Zhang, Di Zou, Gary Cheng, Haoran Xie, Fu Lee Wang, Oliver Tat Sheung Au. Target languages, types of activities, engagement, and effectiveness of extramural language learning. PLOS ONE. 2021; 16 (6):e0253431.
Chicago/Turabian StyleRuofei Zhang; Di Zou; Gary Cheng; Haoran Xie; Fu Lee Wang; Oliver Tat Sheung Au. 2021. "Target languages, types of activities, engagement, and effectiveness of extramural language learning." PLOS ONE 16, no. 6: e0253431.
Knowledge graph as a research topic is increasingly popular to represent structural relations between entities. Recent years have witnessed the release of various open-source and enterprise-supported knowledge graphs with dramatic growth in applying knowledge representation and reasoning into different areas like natural language processing and computer vision. This study aims to comprehensively explore the status and trends – particularly the thematic research structure – of knowledge graphs. Specifically, based on 386 research articles published from 1991 to 2020, we conducted analyses in terms of the (1) visualization of the trends of annual article and citation counts, (2) recognition of major institutions, countries/regions, and publication sources, (3) visualization of scientific collaborations of major institutions and countries/regions, and (4) detection of major research themes and their developmental tendencies. Interest in knowledge graph research has clearly increased from 1991 to 2020 and is continually expanding. China is the most prolific country in knowledge graph research. Moreover, countries/regions and institutions that have higher levels of international collaboration are more impactful. Several widely studied issues such as knowledge graph embedding, search and query based on knowledge graphs, and knowledge graphs for intangible cultural heritage are highlighted. Based on the results, we further summarize perspective directions and suggestions for researchers, practitioners, and project managers to facilitate future research on knowledge graphs.
Xieling Chen; Haoran Xie; Zongxi Li; Gary Cheng. Topic Analysis and Development in Knowledge Graph Research: A Bibliometric Review on Three Decades. Neurocomputing 2021, 1 .
AMA StyleXieling Chen, Haoran Xie, Zongxi Li, Gary Cheng. Topic Analysis and Development in Knowledge Graph Research: A Bibliometric Review on Three Decades. Neurocomputing. 2021; ():1.
Chicago/Turabian StyleXieling Chen; Haoran Xie; Zongxi Li; Gary Cheng. 2021. "Topic Analysis and Development in Knowledge Graph Research: A Bibliometric Review on Three Decades." Neurocomputing , no. : 1.
Understanding word-level emotion in terms of both category and intensity has always been considered an essential step in addressing text emotion classification tasks. Existing studies have mainly adopted the categorical lexicons that are tagged by predefined emotion taxonomies to link affective words with discrete emotions. However, in these lexicons, emotion tags are restricted to a specific set of basic emotions. Moreover, the emotional intensity is ignored, making these methods less flexible and less informative. This paper proposes a novel method to generate a word-level emotion distribution (WED) vector by incorporating domain knowledge and dimensional lexicon. The proposed method can link a word with more generic and fine-grained emotion taxonomies with quantitatively computed intensities. We propose two schemas to utilize the WED vector implicitly and explicitly to facilitate classification. The implicit approach implements a rule-based conversion strategy to augment the information in the label space. The explicit approach exploits WED as an emotional word embedding to enhance the sentiment feature. We conduct extensive experiments on seven multiclass datasets. The results indicate that both proposed schemas produce competitive results compared with the state-of-the-art baselines.
Zongxi Li; Haoran Xie; Gary Cheng; Qing Li. Word-level emotion distribution with two schemas for short text emotion classification. Knowledge-Based Systems 2021, 227, 107163 .
AMA StyleZongxi Li, Haoran Xie, Gary Cheng, Qing Li. Word-level emotion distribution with two schemas for short text emotion classification. Knowledge-Based Systems. 2021; 227 ():107163.
Chicago/Turabian StyleZongxi Li; Haoran Xie; Gary Cheng; Qing Li. 2021. "Word-level emotion distribution with two schemas for short text emotion classification." Knowledge-Based Systems 227, no. : 107163.
Research on sentic computing has received intensive attention in recent years, as indicated by the increased availability of academic literature. However, despite the growth in literature and researchers’ interests, there are no reviews on this topic. This study comprehensively explores the current research progress and tendencies, particularly the thematic structure of sentic computing, to provide insights into the issues addressed during the past decade and the potential future of sentic computing. We combined bibliometric analysis and structural topic modeling to examine sentic computing literature in various aspects, including the tendency of annual article count, top journals, countries/regions, institutions, and authors, the scientific collaborations between major contributors, as well as the major topics and their tendencies. We obtained interesting and meaningful findings. For example, sentic computing has attracted growing interest in academia. In addition, Cognitive Computation and Nanyang Technological University were found to be the most productive journal and institution in publishing sentic computing studies, respectively. Moreover, important issues such as cyber issues and public opinion, deep neural networks and personality, financial applications and user profiles, and affective and emotional computing have been commonly addressed by authors focusing on sentic computing. Our study provides a thorough overview of sentic computing, reveals major concerns among scholars during the past decade, and offers insights into the future directions of sentic computing research.
Xieling Chen; Haoran Xie; Gary Cheng; Zongxi Li. A Decade of Sentic Computing: Topic Modeling and Bibliometric Analysis. Cognitive Computation 2021, 1 -24.
AMA StyleXieling Chen, Haoran Xie, Gary Cheng, Zongxi Li. A Decade of Sentic Computing: Topic Modeling and Bibliometric Analysis. Cognitive Computation. 2021; ():1-24.
Chicago/Turabian StyleXieling Chen; Haoran Xie; Gary Cheng; Zongxi Li. 2021. "A Decade of Sentic Computing: Topic Modeling and Bibliometric Analysis." Cognitive Computation , no. : 1-24.
Computers in Human Behavior (CHB) is a well-established source with a wide range of audiences in the field of human interactions with computers and has been one of the most widely acknowledged and leading venues with significant scientific impact for more than 35 years. This review provides an overview of the status, trends, and particularly the thematic structure of the CHB by adopting bibliometrics and structural topic modeling on 5957 studies. Specifically, we analyzed the trend of publications, identified major institutions and countries/regions, detected scientific collaboration patterns, and uncovered important topics. Significant findings were presented. For example, the contribution of the USA and Open University of Netherlands was highlighted. Important research topics such as e-commerce, social interactions and behaviors, public opinion and social media, cyberbullying, online sexual issues, and game and gamification were identified. This review contributes to the CHB community by justifying the interest in human behavior issues concerning computer use and identifying future research lines on this topic.
Xieling Chen; Di Zou; Haoran Xie; Gary Cheng. A Topic-Based Bibliometric Review of Computers in Human Behavior: Contributors, Collaborations, and Research Topics. Sustainability 2021, 13, 4859 .
AMA StyleXieling Chen, Di Zou, Haoran Xie, Gary Cheng. A Topic-Based Bibliometric Review of Computers in Human Behavior: Contributors, Collaborations, and Research Topics. Sustainability. 2021; 13 (9):4859.
Chicago/Turabian StyleXieling Chen; Di Zou; Haoran Xie; Gary Cheng. 2021. "A Topic-Based Bibliometric Review of Computers in Human Behavior: Contributors, Collaborations, and Research Topics." Sustainability 13, no. 9: 4859.
This paper provided a systematic review of previous Augmented Reality (AR) and Virtual Reality (VR) studies on language learning. A total of 88 articles were selected and analyzed from five perspectives: their ways of integrating AR or VR tools in language learning; main users of AR and VR technologies; major research findings; why AR and VR tools are effective in promoting language learning; and the implications. It was found that (1) immersing learners into virtual worlds is the main approach to language learning in AR and VR studies; (2) university students were the main users of AR/VR technologies; (3) the major research findings concerning the benefits of AR and VR included improvement of students’ learning outcomes, enhancement of motivation, and positive perceptions towards using AR and VR; (4) AR and VR tools promoted language learning through providing immersive learning experience, enhancing motivation, creating interaction, and reducing learning anxiety; and (5) implications identified from previous research include the need of providing training for teachers, enlarging sample sizes, and exploring learner factors such as learner engagement and satisfaction.
Xinyi Huang; Di Zou; Gary Cheng; Haoran Xie. A Systematic Review of AR and VR Enhanced Language Learning. Sustainability 2021, 13, 4639 .
AMA StyleXinyi Huang, Di Zou, Gary Cheng, Haoran Xie. A Systematic Review of AR and VR Enhanced Language Learning. Sustainability. 2021; 13 (9):4639.
Chicago/Turabian StyleXinyi Huang; Di Zou; Gary Cheng; Haoran Xie. 2021. "A Systematic Review of AR and VR Enhanced Language Learning." Sustainability 13, no. 9: 4639.
Chinese word embeddings have recently garnered considerable attention. Chinese characters and their sub-character components, which contain rich semantic information, are incorporated to learn Chinese word embeddings. Chinese characters can represent a combination of meaning, structure, and pronunciation. However, existing embedding learning methods focus on the structure and meaning of Chinese characters. In this study, we aim to develop an embedding learning method that can make complete use of the information represented by Chinese characters, including phonology, morphology, and semantics. Specifically, we propose a pronunciation-enhanced Chinese word embedding learning method, where the pronunciations of context characters and target characters are simultaneously encoded into the embeddings. Evaluation of word similarity, word analogy reasoning, text classification, and sentiment analysis validate the effectiveness of our proposed method.
Qinjuan Yang; Haoran Xie; Gary Cheng; Fu Lee Wang; Yanghui Rao. Pronunciation-Enhanced Chinese Word Embedding. Cognitive Computation 2021, 13, 688 -697.
AMA StyleQinjuan Yang, Haoran Xie, Gary Cheng, Fu Lee Wang, Yanghui Rao. Pronunciation-Enhanced Chinese Word Embedding. Cognitive Computation. 2021; 13 (3):688-697.
Chicago/Turabian StyleQinjuan Yang; Haoran Xie; Gary Cheng; Fu Lee Wang; Yanghui Rao. 2021. "Pronunciation-Enhanced Chinese Word Embedding." Cognitive Computation 13, no. 3: 688-697.
Mesh denoising is a fundamental component of many disparate reverse engineering applications of measurement surfaces. This article presents a cascaded normal filtering neural network (termed a CNF-Net) for geometry-aware mesh denoising of measurement surfaces. CNF-Net leverages the geometry domain knowledge (GDK) that, a mesh approximates to its underlying surface compactly if all mesh facets at most lie on the surface intersections while not crossing them. Benefiting from the well-estimated underlying geometry of noisy mesh facets, a multiscale guidedly filtered normal descriptor (M-GFND) is formulated, and multiple height maps are constructed from the M-GFND. The height maps can be effectively fed into CNF-Net for learning the transformation matrices between the M-GFND and the ground-truth facet normal. CNF-Net can automatically handle meshes with multiscale geometric features yet corrupted by the noise of different distributions, while existing learning-based wisdoms commonly pursue an overall normal estimation accuracy yet fail to preserve surface significant features. Both visual and numerical evaluations on synthetic and real noise data sets consistently show the clear improvements of CNF-Net over the state-of-the-arts.
Dingkun Zhu; Yingkui Zhang; Zhiqi Li; Weiming Wang; Haoran Xie; Mingqiang Wei; Gary Cheng; Fu Lee Wang. Cascaded Normal Filtering Neural Network for Geometry-Aware Mesh Denoising of Measurement Surfaces. IEEE Transactions on Instrumentation and Measurement 2021, 70, 1 -13.
AMA StyleDingkun Zhu, Yingkui Zhang, Zhiqi Li, Weiming Wang, Haoran Xie, Mingqiang Wei, Gary Cheng, Fu Lee Wang. Cascaded Normal Filtering Neural Network for Geometry-Aware Mesh Denoising of Measurement Surfaces. IEEE Transactions on Instrumentation and Measurement. 2021; 70 ():1-13.
Chicago/Turabian StyleDingkun Zhu; Yingkui Zhang; Zhiqi Li; Weiming Wang; Haoran Xie; Mingqiang Wei; Gary Cheng; Fu Lee Wang. 2021. "Cascaded Normal Filtering Neural Network for Geometry-Aware Mesh Denoising of Measurement Surfaces." IEEE Transactions on Instrumentation and Measurement 70, no. : 1-13.
University students must acquire good academic reading skills in undergraduate study. One of the effective skills is to insert annotations in the process of reading. With the advance of web technologies, students can jointly annotate an online article. However, the effectiveness of using online collaborative annotation platforms largely depends on the integration of instructional support. This paper elaborates on the design and effectiveness of using a peer assessment of online collaborative annotation approach in enhancing academic reading skills. Seventy-five first-year undergraduates were invited to annotate an article collaboratively in an online platform. They were requested to carry out peer assessment on the appropriateness of the annotations. Results from the data collected by a questionnaire suggest that the students considered the online platform enhanced collaborative learning, and the peer assessment component helped engage learning. They expressed their preferences to use the online annotation platform collaboratively in their future learning.
Wing Shui Ng; Haoran Xie; Gary Cheng. Enhancing Academic Reading Skills Using a Peer Assessment of Online Collaborative Annotation Approach. Communications in Computer and Information Science 2020, 281 -292.
AMA StyleWing Shui Ng, Haoran Xie, Gary Cheng. Enhancing Academic Reading Skills Using a Peer Assessment of Online Collaborative Annotation Approach. Communications in Computer and Information Science. 2020; ():281-292.
Chicago/Turabian StyleWing Shui Ng; Haoran Xie; Gary Cheng. 2020. "Enhancing Academic Reading Skills Using a Peer Assessment of Online Collaborative Annotation Approach." Communications in Computer and Information Science , no. : 281-292.
Game-based learning and self-regulated learning have long been valued as effective approaches to language education. However, little research has been conducted to investigate their integration, namely, game-based self-regulated language learning (GBSRLL). This study aims to conceptualise GBSRLL based on the combination of theoretical analysis, thematic evolution analysis, and social network analysis on the research articles in the fields of game-based language learning and self-regulated language learning. The results show that GBSRLL is a new interdisciplinary field emerging since the period from 2018 to 2019. Self-regulated learning strategies that can be performed in GBSRLL, the effects of GBSRLL on learners’ affective states, and the features in GBSRLL were the prominent research topics in this field. Its theoretical foundation centres on the positive correlations between learner motivation, self-efficacy, and autonomy and the implementation of game-based learning and self-regulated learning. It is feasible to conduct GBSRLL due to the strong supportiveness of game mechanics for various phases and strategies of self-regulated learning. More contributions to this new interdisciplinary field are called for, especially from the aspects of the long-term effects of GBSRLL on academic performance and the useful tools and technologies for implementing GBSRLL.
Ruofei Zhang; Gary Cheng; Xieling Chen. Game-based self-regulated language learning: Theoretical analysis and bibliometrics. PLOS ONE 2020, 15, e0243827 .
AMA StyleRuofei Zhang, Gary Cheng, Xieling Chen. Game-based self-regulated language learning: Theoretical analysis and bibliometrics. PLOS ONE. 2020; 15 (12):e0243827.
Chicago/Turabian StyleRuofei Zhang; Gary Cheng; Xieling Chen. 2020. "Game-based self-regulated language learning: Theoretical analysis and bibliometrics." PLOS ONE 15, no. 12: e0243827.
Personalized learning has become an important and powerful paradigm catering for various needs, styles, preferences, and modes of learning. Several methods including task recommendations and path planning have recently emerged to effectively implement personalized learning using e-learning systems. The literature shows that the use of task recommendations in e-learning systems is a very effective way to facilitate personalized vocabulary learning. One of the key research issues regarding these personalized vocabulary learning systems is how to model the learning logs of each learner. Specifically, how to measure the learning effectiveness of each learned tasks has become a core issue for establishing personalized learning systems. Three theories, Spaced Learning (SL), Technique Feature Analysis (TFA), and Involvement Load Hypothesis (ILH), are commonly applied for achieving this purpose. In this study, we compared the effectiveness of these three linguistic theories for modeling EFL learners’ personalized vocabulary learning via task recommendations. By conducting experimental studies among different groups of participants, our findings revealed that the ILH and TFA were more suitable than SL for facilitating personalized vocabulary learning. It is therefore suggested that future personalized vocabulary learning systems ought to be designed and developed based on comprehensive theoretical frameworks such as the ILH and TFA.
Di Zou; Minhong Wang; Haoran Xie; Gary Cheng; Fu Lee Wang; Lap-Kei Lee. A comparative study on linguistic theories for modeling EFL learners: facilitating personalized vocabulary learning via task recommendations. Interactive Learning Environments 2020, 29, 270 -282.
AMA StyleDi Zou, Minhong Wang, Haoran Xie, Gary Cheng, Fu Lee Wang, Lap-Kei Lee. A comparative study on linguistic theories for modeling EFL learners: facilitating personalized vocabulary learning via task recommendations. Interactive Learning Environments. 2020; 29 (2):270-282.
Chicago/Turabian StyleDi Zou; Minhong Wang; Haoran Xie; Gary Cheng; Fu Lee Wang; Lap-Kei Lee. 2020. "A comparative study on linguistic theories for modeling EFL learners: facilitating personalized vocabulary learning via task recommendations." Interactive Learning Environments 29, no. 2: 270-282.
Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AI-assisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AI-assisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AI-assisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.
Xieling Chen; Juan Chen; Gary Cheng; Tao Gong. Topics and trends in artificial intelligence assisted human brain research. PLOS ONE 2020, 15, e0231192 .
AMA StyleXieling Chen, Juan Chen, Gary Cheng, Tao Gong. Topics and trends in artificial intelligence assisted human brain research. PLOS ONE. 2020; 15 (4):e0231192.
Chicago/Turabian StyleXieling Chen; Juan Chen; Gary Cheng; Tao Gong. 2020. "Topics and trends in artificial intelligence assisted human brain research." PLOS ONE 15, no. 4: e0231192.
Natural language processing (NLP) is an effective tool for generating structured information from unstructured data, the one that is commonly found in clinical trial texts. Such interdisciplinary research has gradually grown into a flourishing research field with accumulated scientific outputs available. In this study, bibliographical data collected from Web of Science, PubMed, and Scopus databases from 2001 to 2018 had been investigated with the use of three prominent methods, including performance analysis, science mapping, and, particularly, an automatic text analysis approach named structural topic modeling. Topical trend visualization and test analysis were further employed to quantify the effects of the year of publication on topic proportions. Topical diverse distributions across prolific countries/regions and institutions were also visualized and compared. In addition, scientific collaborations between countries/regions, institutions, and authors were also explored using social network analysis. The findings obtained were essential for facilitating the development of the NLP-enhanced clinical trial texts processing, boosting scientific and technological NLP-enhanced clinical trial research, and facilitating inter-country/region and inter-institution collaborations.
Xieling Chen; Haoran Xie; Gary Cheng; Leonard K. M. Poon; Mingming Leng; Fu Lee Wang. Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis. Applied Sciences 2020, 10, 2157 .
AMA StyleXieling Chen, Haoran Xie, Gary Cheng, Leonard K. M. Poon, Mingming Leng, Fu Lee Wang. Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis. Applied Sciences. 2020; 10 (6):2157.
Chicago/Turabian StyleXieling Chen; Haoran Xie; Gary Cheng; Leonard K. M. Poon; Mingming Leng; Fu Lee Wang. 2020. "Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis." Applied Sciences 10, no. 6: 2157.
Computers & Education has been leading the field of computers in education for over 40 years, during which time it has developed into a well-known journal with significant influences on the educational technology research community. Questions such as “in what research topics were the academic community of Computers & Education interested?” “how did such research topics evolve over time?” and “what were the main research concerns of its major contributors?” are important to both the editorial board and readership of Computers & Education. To address these issues, this paper conducted a structural topic modeling analysis of 3963 articles published in Computers & Education between 1976 and 2018 bibliometrically. A structural topic model was used to profile the research hotspots. By further exploring annual topic proportion trends and topic correlations, potential future research directions and inter-topic research areas were identified. The major research concerns of the publications in Computers & Education by prolific countries/regions were shown and compared. Thus, this work provided useful insights and implications, and it could be used as a guide for contributors to Computers & Education.
Xieling Chen; Di Zou; Gary Cheng; Haoran Xie. Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education. Computers & Education 2020, 151, 103855 .
AMA StyleXieling Chen, Di Zou, Gary Cheng, Haoran Xie. Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education. Computers & Education. 2020; 151 ():103855.
Chicago/Turabian StyleXieling Chen; Di Zou; Gary Cheng; Haoran Xie. 2020. "Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education." Computers & Education 151, no. : 103855.
Gary Cheng. Exploring the effects of automated tracking of student responses to teacher feedback in draft revision: evidence from an undergraduate EFL writing course. Interactive Learning Environments 2019, 1 -23.
AMA StyleGary Cheng. Exploring the effects of automated tracking of student responses to teacher feedback in draft revision: evidence from an undergraduate EFL writing course. Interactive Learning Environments. 2019; ():1-23.
Chicago/Turabian StyleGary Cheng. 2019. "Exploring the effects of automated tracking of student responses to teacher feedback in draft revision: evidence from an undergraduate EFL writing course." Interactive Learning Environments , no. : 1-23.
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.
Gary Cheng; Wing Shui Ng. Secondary Students’ Views on Using Flipped Classroom to Learn Computer Programming: Lessons Learned in a Mixed Methods Study. Communications in Computer and Information Science 2019, 27 -36.
AMA StyleGary Cheng, Wing Shui Ng. Secondary Students’ Views on Using Flipped Classroom to Learn Computer Programming: Lessons Learned in a Mixed Methods Study. Communications in Computer and Information Science. 2019; ():27-36.
Chicago/Turabian StyleGary Cheng; Wing Shui Ng. 2019. "Secondary Students’ Views on Using Flipped Classroom to Learn Computer Programming: Lessons Learned in a Mixed Methods Study." Communications in Computer and Information Science , no. : 27-36.
There has recently been a renewed interest in integrating programming into the curriculum of primary education, partly due to the availability of the visual programming environment (VPE) designed for educational purposes. While substantial progress on exploring the potential benefits of VPE has been achieved, much remains to be done to understand students’ acceptance of VPE and whether gender difference plays a role in their acceptance. This study was thus designed to extend the technology acceptance model to identify determinants influencing boys’ and girls’ behavioural intention to use VPE in the primary school context. It used a mixed method approach to evaluate the proposed model using questionnaire and interview data collected from 431 students (296 boys and 135 girls) in 38 primary schools. Among boys and girls, computer self-efficacy is shown to be the external factor significantly influencing both perceived usefulness and perceived ease of use of VPE, while attitude towards VPE is found to have a significant effect on behavioural intention to use VPE. In addition, gender differences are found in the impact of social influence and external encouragement on students’ perceptions towards VPE, and also in the impact of students’ perceptions towards VPE on their behavioural intention to use it. Based on the findings, several recommendations are made to encourage primary students to use VPE for programming.
Gary Cheng. Exploring factors influencing the acceptance of visual programming environment among boys and girls in primary schools. Computers in Human Behavior 2018, 92, 361 -372.
AMA StyleGary Cheng. Exploring factors influencing the acceptance of visual programming environment among boys and girls in primary schools. Computers in Human Behavior. 2018; 92 ():361-372.
Chicago/Turabian StyleGary Cheng. 2018. "Exploring factors influencing the acceptance of visual programming environment among boys and girls in primary schools." Computers in Human Behavior 92, no. : 361-372.
Gary Cheng; Julia Chen; Dennis Foung; Vincent Lam; Michael Tom. Towards Automatic Classification of Teacher Feedback on Student Writing. International Journal of Information and Education Technology 2018, 8, 342 -346.
AMA StyleGary Cheng, Julia Chen, Dennis Foung, Vincent Lam, Michael Tom. Towards Automatic Classification of Teacher Feedback on Student Writing. International Journal of Information and Education Technology. 2018; 8 (5):342-346.
Chicago/Turabian StyleGary Cheng; Julia Chen; Dennis Foung; Vincent Lam; Michael Tom. 2018. "Towards Automatic Classification of Teacher Feedback on Student Writing." International Journal of Information and Education Technology 8, no. 5: 342-346.
This study aimed to develop an automatic classification system, namely ACTIVE, for generating immediate and individualised feedback on students’ reflective entries about their second language (L2) learning experiences. It also aimed to explore students’ attitudes towards using the system to support the development of their reflective skills in L2 learning. A total of 466 undergraduate students took part in the study. One hundred and twenty-seven participants were involved in the development phase, where their reflective entries were manually annotated according to a classification framework for critical reflection on L2 learning, and the annotated entries were then used to develop the ACTIVE system. The remaining participants were asked to generate automated feedback reports on their reflective entries for improvement by using the system. To solicit their views towards the system, the participants were administered an online questionnaire and some of them were also invited to attend a semi-structured interview. The overall results indicate that the classification accuracy of the system is comparable to that of human annotators. They also suggest that both teacher and machine feedback types have strengths and limitations, highlighting the need to further explore the use of multi-channel, multi-layer feedback in improving students’ reflective skills in L2 learning.
Gary Cheng. Towards an automatic classification system for supporting the development of critical reflective skills in L2 learning. Australasian Journal of Educational Technology 2016, 1 .
AMA StyleGary Cheng. Towards an automatic classification system for supporting the development of critical reflective skills in L2 learning. Australasian Journal of Educational Technology. 2016; ():1.
Chicago/Turabian StyleGary Cheng. 2016. "Towards an automatic classification system for supporting the development of critical reflective skills in L2 learning." Australasian Journal of Educational Technology , no. : 1.