2022, Volume 25, Issue 1

Special Issue on "Understanding and Bridging Gap in Multi-mode Digital Learning during Post-Pandemic Recovery"

Guest Editor(s): Jun Shen, Alex Shvonski, Tingru Cui and Samuel Fosso Wamba

Full Length Articles

Chih-Hung Chen

Master Program of Professional Teacher, National Taichung University of Education, Taiwan // duke.chchen@gmail.com

Chorng-Shiuh Koong

Department of Computer Science, National Taichung University of Education, Taiwan // csko@mail.ntcu.edu.tw

Chien Liao

Master Program of Professional Teacher, National Taichung University of Education, Taiwan // btp107203@gm.ntcu.edu.tw


Artificial intelligence (AI) technology has been progressively utilized in educational environments in recent years, due to the advances in computing and information processing techniques. The automatic speech recognition technique (ASR) provides students with instantaneous feedback and interactive oral practice for supporting a context with self-paced learning. Corrective feedback (CF) should be combined with ASR-based systems to enhance students’ speaking performance, and to reduce their cognitive load. However, learners’ perceptions of CF are mixed, and CF might give rise to learning anxiety. In this study, a dynamic assessment-based speech recognition (called DA-SR) learning system was designed to facilitate students’ English speaking. Moreover, a quasi-experiment was implemented to evaluate the effects of the proposed approach on students’ speaking learning effectiveness, via respectively providing the DA-SR and the corrective feedback-based speech recognition (called CF-SR) approaches for the experimental and control groups. The experimental results revealed that both the DA-SR group and the CF-SR group can effectively improve the students’ English speaking skills, and decrease their English speaking learning anxiety. Moreover, this study further demonstrated that the DA-SR approach successfully reduced students’ English class performance anxiety, and extraneous cognitive load in comparison with the CF-SR approach. It could be a valuable reference for designing English speaking learning activities in EFL learning environments.


Artificial intelligence, Speech recognition, Corrective feedback, Dynamic assessment, Learning anxiety

Ching-Yi Chang

School of Nursing, College of Nursing, Taipei Medical University, Taiwan // Department of Nursing, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan, Republic of China // frinng.cyc@gmail.com

Shu-Yu Kuo

School of Nursing, College of Nursing, Taipei Medical University, Taiwan // sykuo@tmu.edu.tw

Gwo-Haur Hwang

Bachelor Program in Industrial Technology, National Yunlin University of Science and Technology, Taiwan // ghhwang0424@gmail.com


Conventional nursing courses have solely adopted lecture-based instruction for knowledge delivery, which tends to lack interaction, rehearsal, and personalized feedback. The development of chatbot technologies and their broad application have provided an opportunity to solve the abovementioned problems. Some knowledge-based chatbot systems have been developed; however, it is still a challenging issue for researchers to determine exactly how to effectively apply these chatbot technologies in nursing training courses. Intending to explore the application mode of chatbot technologies and their effectiveness in nursing education, this study integrated a knowledge-based chatbot system into the teaching activities of a physical examination course, using smartphones as the learning devices, and guiding students to practice their anatomy knowledge in addition to analyzing their learning efficacy and pleasure. A quasi-experiment was conducted by recruiting two classes of university students with nursing majors. One class was the experimental group learning with the knowledge-based chatbot system, while the other class was the control group learning with the traditional instruction. Based on the experimental results, the knowledge-based chatbot system effectively enhanced students’ academic performance, critical thinking, and learning satisfaction. The results indicate that the application of chatbots has great potential in nursing education.


Chatbots, Knowledge-based chatbot system, Nursing Training, Mobile learning

Xieling Chen

Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR // xielingchen0708@gmail.com

Di Zou

Department of English Language Education, The Education University of Hong Kong, Hong Kong SAR // dizoudaisy@gmail.com

Haoran Xie

Department of Computing and Decision Sciences, Lingnan University, Hong Kong SAR // hrxie2@gmail.com

Gary Cheng

Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR // chengks@eduhk.hk

Caixia Liu

Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR // Institute of EduInfo Science & Engineering, Nanjing Normal University, China // cxsqz@126.com


With the increasing use of Artificial Intelligence (AI) technologies in education, the number of published studies in the field has increased. However, no large-scale reviews have been conducted to comprehensively investigate the various aspects of this field. Based on 4,519 publications from 2000 to 2019, we attempt to fill this gap and identify trends and topics related to AI applications in education (AIEd) using topic-based bibliometrics. Results of the review reveal an increasing interest in using AI for educational purposes from the academic community. The main research topics include intelligent tutoring systems for special education; natural language processing for language education; educational robots for AI education; educational data mining for performance prediction; discourse analysis in computer-supported collaborative learning; neural networks for teaching evaluation; affective computing for learner emotion detection; and recommender systems for personalized learning. We also discuss the challenges and future directions of AIEd.


Artificial intelligence in education, Structural topic modeling, Bibliometric analysis, Research topics, Research evolution

Jiansheng Li

School of Education Science, Nanjing Normal University, Nanjing, China // 2869753244@qq.com

Jiao Liu

School of Education Science, Nanjing Normal University, Nanjing, China // 2776571510@qq.com

Rui Yuan

School of Education Science, Nanjing Normal University, Nanjing, China // 1622529878@qq.com

Rustam Shadiev

School of Education Science, Nanjing Normal University, Nanjing, China // rustamsh@gmail.com


This study explores the role of socially shared regulation on computational thinking performance in cooperative learning. Ninety-four middle school students from China aged between 16 and 18 participated in this study. Forty-six students were in the experimental group, and 48 students were in the control group. Students in the experimental group learned under the socially shared regulation of learning (SSRL) condition, which included planning and goal setting, task and content monitoring, and task and content evaluation. Students in the control group learned in a traditional way. The results showed that the students in the experimental group significantly outperformed their counterparts on the midtest and posttest. Additionally, the learning gain of the experimental group was much better from the pretest to the midtest. Different subgroups in the experimental group had different learning performances, and task monitoring and content monitoring were two important SSRL processes that led to improved computational thinking performance. Our results suggest that SSRL is beneficial for learning computational thinking subjects. Throughout the process of SSRL, different groups have different learning dynamics, and task and content monitoring plays a major role in computational thinking performance.


Socially shared regulation, Group monitoring, Cooperation, Computational thinking, Learning performance

Hui-Tzu Chang

Center for Institutional Research and Data Analytics, National Yang Ming Chiao Tung University, Hsinchu // simple@nycu.edu.tw

Chia-Yu Lin

Department of Computer Science and Engineering, Yuan Ze University, Taoyuan // sallylin0121@gmail.com

Li-Chun Wang

Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu // wang@nycu.edu.tw

Fang-Ching Tseng

Electrical Engineering and Computer Science Undergraduate Honors Program, National Yang Ming Chiao Tung University, Hsinchu // claire.eecs07@nctu.edu.tw


In this study, we built a personalized hybrid course recommendation system (PHCRS) that considers students’ interests, abilities and career development. To meet students’ individual needs, we adopted the five most widely used algorithms, including content-based filtering, popularity-based methods, item-based collaborative filtering, user-based collaborative filtering, and score-based methods, to build a PHCRS. First, we collected course syllabi and labeled each course (e.g., knowledge/skills taught, basic/advanced level). Next, we used course labels and students’ past course selections and grades to train five recommendation models. To evaluate the accuracy of the system, we performed experiments with students in the Department of Electrical and Computer Engineering, which provides 1794 courses for 925 students and utilizes the receiver operating characteristic curve (ROC) and normalized discounted cumulative gain (NDCG) as metrics. The results showed that our proposed system can achieve accuracies of 80% for ROC and 90% for NDCG. We invited 46 participants to test our system and complete a questionnaire. Overall, 60 to 70% of participants were interested in the recommended courses, while the course recommendation lists produced by content-based filtering were in line with 67.40% of students’ actual course preferences. This study also found that students were more interested in courses at the top of the recommendation lists, and more students were autonomously motivated than held extrinsic informational motivation across the five recommendation methods. These findings highlighted that the proposed course recommendation system can help students choose the courses that interest them most.


Course recommendation, Course selection, Learning aids, Personalized learning


Jun Shen

University of Wollongong, Australia // jshen@uow.edu.au

Alex Shvonski

Massachusetts Institute of Technology, USA // shvonski@mit.edu

Tingru Cui

University of Melbourne, Australia // tingru.cui@unimelb.edu.au

Samuel Fosso Wamba

Toulouse Business School, France // s.fosso-wamba@tbs-education.fr


COVID-19 pandemic had changed the world-wide education landscape as the whole society is adapting to the “new normal.” We orgainised a special issue collecting research papers that shed insights on how teaching and learning designs will be affected, and how novel educational technologies will help in a fast post-pandemic recovery. 26 papers were received but only 11 papers were finally selected to publish, after two rounds of rigorous reviews. This editorial note discusses the background, quality management and thematic topic groups of the papers.


Multi-mode learning, Post-pandemic, Pedagogical, Learning behavior

Special Issue Articles

Diana Pérez-Marín

Escuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, Spain // diana.perez@urjc.es

Maximiliano Paredes-Velasco

Escuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, Spain // maximiliano.paredes@urjc.es

Celeste Pizarro

Escuela Superior de Ciencias Experimentales y Tecnología, Universidad Rey Juan Carlos, Spain // celeste.pizarro@urjc.es


In this paper, a multi-mode digital teaching approach is proposed based on the use of the VARK (Visual, Aural, Read/Write, Kinaesthetic) model where students have different styles (one or more) that improve their learning (face-to-face and online). Our research question is on the effectiveness of this approach in terms of learning efficacy and students’ satisfaction. An experiment with 41 students has been carried out for five months to answer the research question and to provide a first validation of using VARK for multi-mode digital HCI teaching. During the experiment, the theoretical sessions were given through videoconference using Microsoft Teams and with the support of Moodle. In the practical sessions, students had to create a software prototype following a User-Centred Design with a real client. For this, they used Discord to collaborate in their groups, Teams to ask questions to teachers and PowerPoint and Genially to present their work online to the class through a Teams videoconference. A regression model has been provided to predict the VARK indicated by the questionnaire to each student with a prediction success of nearly 77%. Using the VARK multi-mode digital teaching approach has proved valid, and effective and beneficial in the teaching of HCI with a significant improvement in the learning scores and satisfaction levels of the students even with respect to pre-COVID-19 where the teaching was face-to-face.


Multi-mode digital teaching, VARK, Human-Computer Interaction, COVID-19

Toni McLaughlan

Lancaster University, United Kingdom // t.mclaughlan@lancaster.ac.uk


This paper evaluates an international, online, content skills-based teacher education program sponsored by the U.S. State Department. The evaluation was designed using a RUFDATA framework (Saunders, 2000) to facilitate a complete, reflective assessment of the target program. Establishing causes-and-effects of the program’s performance and data analysis involved adoption of a Contribution Analysis (Mayne, 2001; Mayne, 2008). Utilizing the six steps detailed in Mayne (2012), a credible contribution story emerged, highlighting strengths and weaknesses of transitioning teacher-training programs to virtual platforms. This evaluation has implications for teachers, teacher trainers, professionals planning similar programs particularly in developing regions, and individuals interested in how theory can be applied practically to impact continued teacher education processes. This paper contributes to knowledge as there are few formal evaluations of online international teacher education programs that facilitate observation of all aspects of a virtual course over an extended period of time and provide small-group engagement with course creators, especially with populations straddling the digital divide. It is also the first to conduct a theory-based evaluation of a U.S. English Language Specialist project despite the program’s 1991 inception and current running rate of 150-200 projects annually worldwide (U.S. Department of State, 2021).


Teacher training, Educational technology, Online, Developing regions, Contribution Analysis

Biyun Huang

Centre for Learning Sciences and Technologies, The Chinese University of Hong Kong, Hong Kong SAR, China // lucyhuang99@cuhk.edu.hk

Morris Siu-Yung Jong

Centre for Learning Sciences and Technologies, The Chinese University of Hong Kong, Hong Kong SAR, China // Department of Curriculum and Instruction, The Chinese University of Hong Kong, Hong Kong SAR, China // mjong@cuhk.edu.hk

Ching Sing Chai

Department of Curriculum and Instruction, The Chinese University of Hong Kong, Hong Kong SAR, China // cschai@cuhk.edu.hk


The COVID-19 pandemic has brought disruptions and constraints to K-12 STEM education, such as the shortened classroom time and the restrictions on classroom interactions. More empirical evidence is needed to inform educators and practitioners which strategies work, and which do not ensure optimal learning in the pandemic circumstance. In response to the call for more empirical evidence and the need for cultivating responsible and competent 21st century citizens, we designed and implemented a transdisciplinary STEM curriculum during the COVID-19 outbreak. In order to facilitate the smooth delivery of the learning contents and to authentically engage learners in the learning process, multi-model video approaches were employed with consideration to the different characteristics of the three disciplines: STEM, social service, and writing. The pre- and post-test results indicated that students’ transdisciplinary STEM knowledge improved significantly after studying the curriculum. The integration of STEM, social service, and writing disciplines promoted the growth of students’ empathy, interest, and self-efficacy. Apart from appreciating this learning opportunity, students expressed that their STEM knowledge and empathy were both enhanced. It is believed that some implementation strategies are also applicable even when the standard teaching order is restored in the post-COVID-19 era.


STEM, Transdisciplinary STEM, Video-facilitated approach, Social service, Empathy

Albert Rof

University of Girona, Spain // u1008518@campus.udg.edu

Andrea Bikfalvi

University of Girona, Spain // andrea.bikfalvi@udg.edu

Pilar Marques

University of Girona, Spain // pilar.marques@udg.edu


The COVID-19 pandemic has forced the digitalization of the majority of universities, prior to which they were largely operating using face-to-face modes of learning. Increased competition in the digital environment places universities under greater pressure to offer an innovative learning experience. The purpose of this paper is to understand the effects of the sudden pandemic on the ongoing process of digital transformation (DT) and how the learning value proposition of higher education institutions (HEIs) has been affected. The research is based on a single case study of a born digital university, focusing on the changes made to the learning value proposition, and particularly to the multimode learning offer. The paper uncovers the relation between multimodality and customized and personalized learning, all of which are dependent on the use of digital educational technology. The originality of this paper is its longitudinal look at a single case, observing how the significant DT process already underway prior to the pandemic has been impacted by it, accelerating the process, and clarifying the envisaged post-pandemic future for HEIs. Another distinctive aspect is the consideration of the learning proposition as a core element and part of a larger and interdependent value proposition within the overall HEIs business model.


Higher Education Institutions, Customized learning, Multimode learning, Digital transformation, Business model

Jon-Chao Hong

Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taiwan // Department of Industrial Education, National Taiwan Normal University, Taiwan // tcdahong@gmail.com

Xiaohong Liu

School of Education Science, Nanjing Normal University, China // xiaohongliu1211@gmail.com

Wei Cao

School of Education Science, Nanjing Normal University, China // 2576901707@qq.com

Kai-Hsin Tai

Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taiwan // Department of Industrial Education, National Taiwan Normal University, Taiwan // star99xin@gmail.com

Li Zhao

School of Education Science, Nanjing Normal University, China // li.zhao@njnu.edu.cn


With the outbreak of COVID-19, more online learning has been adopted for distance learning. However, the effectiveness of online learning for those students engaged in it for the first time has not been discussed. This study aims to investigate perceived ineffectiveness of online learning and its antecedents related to cognitive and affective factors. Internet self-efficacy (ISE) and Self-efficacy of interacting with learning content (SEILC) were hypothesized to have a correlation with perceived ineffectiveness of online learning (PIOL) mediated by participating Internet cognitive fatigue (ICF) and mind-unwandered, and that ICF was hypothesized to have a correlation with mind-unwandered. Data of 251 students collected from high schools in China during the lockdown period of COVID-19 were subjected to confirmatory factor analysis via AMOS. Results indicated that participants’ ISE and SEILC were positively related to mind-unwandered, but negatively related to ICF during online learning; ICF was positively associated with PIOL; on the other hand, mind-unwandered was negatively associated with PIOL. Furthermore, students’ ISE and SEILC indirectly affect their PIOL mediated by ICF. Findings suggest that an enhancement of learners’ ISE and SEILC could reduce the level of PIOL the first time that online learners were under the COVID-19 lockdown to promote learning effectiveness. This understanding will be useful in case of another pandemic outbreak.


E-learning, High school students, Internet cognitive fatigue, Mind-unwandered, Online learning

Chih-Hung Lin

Master Program in Mathematics and Science Education, Department of Education, National Chiayi University, Taiwan // chuck@mail.ncyu.edu.tw

Wun-Hau Wu

Chiayi County Jhuci Senior High School, Taiwan // willy76616@gmail.com

Tsu-Nan Lee

Taipei Municipal Linong Elementary School, Taiwan // tedbob51@gmail.com


During the worldwide pandemic of coronavirus disease 2019 (COVID-19 pandemic), online learning is increasingly vital for students to learn at home, and online learning platforms provide learning opportunities to students. The Junyi Academy online platform is an online learning platform that both helps lower-achieving students review lessons and helps teachers in Taiwan do differentiated instruction. Several studies have shown the relationships between students’ attention and their academic achievements for students’ self-learning, but how to best use these platforms to help students learn by themselves is unclear. Therefore, this study investigates the relationships between students’ attention and their academic achievements with two online learning environments. A total of 38 upper secondary students in Taiwan to participate in this study, and these students were divided between a Khan-style video lecture (VL) group and an online practice (OP) group. This study adopted an experimental design with data collected by an electroencephalogram (EEG). The results show that students’ attention in the VL group was higher than in the OP group. Furthermore, their attention in three stages differed between the two groups. Student attention was similar in the two groups for the first stage, but the VL group had higher attention for the second and third stages was than did the OP group. In addition, there was no relationship between students’ attention and their academic achievements in the VL and OP groups. Finally, this study raised some suggestions the future research.


Attention, Video lecture, Online practice, Online learning platform, Electroencephalogram (EEG)

Yulong Sun

Department of Educational Technology, College of Education, Shanghai Normal University, China // sunyulong32@163.com

Chun-Hao Chang

Department of Allied Health Education and Digital Learning, National Taipei University of Nursing and Health Sciences, Taiwan // chunhao@gm.ntunhs.edu.tw

Feng-Kuang Chiang

Department of Educational Technology, College of Education, Shanghai Normal University, China // fkchiang@shnu.edu.cn


Previous studies on the topic of “Problem solving” indicate it’s one of the skills of students in the 21st century that educational robots can effectively support. Additionally, there are gender differences in the problem-solving process. Understanding the problem-solving process and using knowledge to solve problems is key to improving one’s problem-solving ability. We therefore conducted a study with 69 fifth graders aimed at exploring whether educational robots can help students improve their understanding of problem-solving process in the context of life sciences. The Intervention was carried out as five learning modules on “Human systems,” and each module corresponded to different stages of engineering design practice. Our data analysis investigated the changes of problem-solving process with two independent variables: different genders and robot learning basis. The results showed educational robots can help students more effectively comprehend life science knowledge and understand the problem-solving process. By contrast, there are differences in the problem-solving process between females and males, and the robot learning basis can help students better articulate the problem-solving process. Although this study provided empirical evidence that educational robots can enhance the learning and problem solving skills for primary school children, future studies need to further explore the differences in problem-solving process from multiple perspectives to improve teaching and curriculum design practices.


Problems-solving process, Educational robots, Primary school, Life sciences, Engineering design

Shouchao Guo, Xiao Wang, Wenbo Deng, Jialing Hong, Jiawen Wang and Yonghe Wu

Shouchao Guo

Department of Education Information Technology, East China Normal University, China // shouchaoguo@126.com

Xiao Wang

Department of Education Information Technology, East China Normal University, China // 3245794069@qq.com

Wenbo Deng

School of Education, Shaanxi Normal University, China // wenbolz@163.com

Jialing Hong

Department of Education Information Technology, East China Normal University, China // 51204108009@stu.ecnu.edu.cn

Jiawen Wang

Department of Education Information Technology, East China Normal University, China // wangjiawen_123@163.com

Yonghe Wu

Department of Education Information Technology, East China Normal University, China // yhwu@deit.ecnu.edu.cn


Three-dimensional (3D) design can improve students’ spatial ability, but the research on the differences of spatial ability development after 3D design training for students with different initial spatial ability is not unified. The ability-as-enhancer hypothesis and the ability-as-compensator hypothesis explain the performance differences of students with different initial spatial abilities in different situations. However, the existing research has not formed a consistent conclusion, which makes students lack of fine guidance, and it is difficult to achieve good spatial ability training effect. This study first explored the differences of students’ performance under different educational interventions, and verified the value of process data in the cultivation of spatial ability. Then, we collected more students’ data, discussed the improvement of students’ spatial ability by 3D design with different initial spatial ability, and tried to explain the difference of students’ performance by students’ 3D design behavior. We found that different educational interventions can affect students’ task participation, and then the effect of spatial ability training. Students with different initial spatial abilities still have significant differences in spatial ability after 3D design, but there is no significant difference in the improvement of spatial ability, and no difference in the data of 3D design operation process. Through cluster analysis, this study also found five types of students in the process of 3D design. There are significant differences in the pre-test, post-test only among some types of students. This study provides a reference for the training effect evaluation of students with different initial spatial abilities.


3D design, Spatial ability, Learning analysis, Ability difference

Binbin Yong

School of Information Science and Engineering, Lanzhou University, Lanzhou, China // yongbb@lzu.edu.cn

Xuetao Jiang

School of Information Science and Engineering, Lanzhou University, Lanzhou, China // jiangxt18@lzu.edu.cn

Jiayin Lin

School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia // jl461@uowmail.edu.au

Geng Sun

Vermilion Cloud, Surrey Hills, NSW, Australia // gs147@uowmail.edu.au

Qingguo Zhou

School of Information Science and Engineering, Lanzhou University, Lanzhou, China // zhouqg@lzu.edu.cn


Deep learning (DL), as the core technology of artificial intelligence (AI), has been extensively researched in the past decades. However, practical DL education needs large marked datasets and computing resources, which is generally not easy for students at school. Therefore, due to training datasets and computing resources restrictions, it is still challenging to popularize DL education in colleges and universities. This paper considers solving this problem by collective intelligence from a resource sharing perspective. In DL, dataset marking and model training both require high workforce and computing power, which may implement through a resource sharing mechanism using collective intelligence. As a test, we have designed a DL education scheme based on collective intelligence under the background of artistic creation to collect teaching materials for DL education. Also, we elaborate on the detailed methods of sharing mechanisms in this article and discuss some related problems to verify this shared learning mechanism.


Deep learning education, Datasets and computing resources, Collective intelligence, Resource sharing perspective

Jiayin Lin

University of Wollongong, Wollongong, NSW, Australia // jl461@uowmail.edu.au

Geng Sun

University of Wollongong, Wollongong, NSW, Australia // gsun@uow.edu.au

Ghassan Beydoun

University of Technology Sydney, Sydney, NSW, Australia // ghassan.beydoun@uts.edu.au

Li Li

Southwest University, Chongqing, China // lily@swu.edu.cn


A newly emerged micro learning service offers a flexible formal, informal, or non-formal online learning opportunity to worldwide users with different backgrounds in real-time. With the assist of big data technology and cloud computing service, online learners can access tremendous fine-grained learning resources through micro learning service. However, big data also causes serious information overload during online learning activities. Hence, an intelligent recommender system is required to filter out not-suitable learning resources and pick the one that matches the learner’s learning requirement and academic background. From the perspective of natural language processing (NLP), this study proposed a novel recommender system that utilises machine translation and language modelling. The proposed model aims to overcome the defects of conventional recommender systems and further enhance distinguish ability of the recommender system for different learning resources.


Information filtering, Recommender system, Micro learning, Big data, Natural language processing

Xuesong Zhai, Xiaoyan Chu, Nanxi Meng, Minjuan Wang, Michael Spector, Chin-Chung Tsai and Hui Liu

Xuesong Zhai

College of Education, Zhejiang University, Hangzhou, Mainland China // xszhai@zju.edu.cn

Xiaoyan Chu

College of Education, Zhejiang University, Hangzhou, Mainland China // xiaoyan_chu@zju.edu.cn

Nanxi Meng

World languages, Literatures and Cultures, University of North Texas, Denton, US // nanxi.meng@unt.edu

Minjuan Wang

Learning Design and Technology, San Diego State University, San Diego, US // mwang@sdsu.edu

Michael Spector

College of Information, University of North Texas, Denton, US // Mike.Spector@unt.edu

Chin-Chung Tsai

Program of Learning Sciences and Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taipei, Taiwan // tsaicc@ntnu.edu.tw

Hui Liu

College of Education, Zhejiang University, Hangzhou, Mainland China // autumnhuihui@zju.edu.cn


Metacognition is regarded as a retrospective skill promoting learners’ learning performance, deep thinking, and academic well-being. Stimulated Recall (SR) is regarded as a reliable approach to inspiring learners’ metacognition in the classroom. However, the outbreak of COVID-19, causing widespread class suspension, may impair the effect of SR on cultivating distance learners’ metacognition. The current study, employing multi-mode stimuli of learners’ eye movements and feedforward, aimed to develop the effect of SR on activating learners’ metacognition in remote settings. Forty-eight university graduates were recruited to participate in an eye-tracking experiment using digital dictionaries. Their feedforward and eye movements were collected as multi-mode stimuli. By reviewing the consistency and discrepancies between their feedforward and eye movements, participants were invited to conduct an SR interview, which stimulated them to retrospect on their prior cognitive behaviors. The results of the metacognition scale pre-post test showed that learners’ metacognitive skills were significantly improved by the stimulated recall with multi-mode stimuli. The findings theoretically enrich the metacognition strategy in the Cognitive Theories of Multimedia Learning, and practically extend the implementation of stimulated recall in distance learning contexts.


Metacognition, Multi-mode stimuli, Stimulated recall, Eye tracking, Digital dictionary

Starting from Volume 17 Issue 4, all published articles of the journal of Educational Technology & Society are available under Creative Commons CC-BY-ND-NC 3.0 license.