The online discussion format has proven itself to be useful for promoting student collaboration and for accomplishing better learning outcomes. As a popular instant message software in China, WeChat ...has been commonly adopted for supporting academic group discussions. Many studies have explored how teacher facilitation affects students' learning performance in the synchronous or asynchronous discussion settings. However, the conclusions are not consistent and there is relative less research about the effects of teacher role on student engagement in the context of online discussion using the semi-synchronous tool-WeChat. This study began by identifying three dimensions of student engagement: behavioral engagement, cognitive engagement, and emotional engagement. A quasi-experiment was then conducted to compare student engagement among experiment groups (with teacher facilitation) and control groups (without teacher facilitation). A total of 46 college students were divided into eight groups and asked to discuss two specific topic tasks. To evaluate participants' behavioral and cognitive engagement, this study used a more objective data analysis method by applying content analysis rather than only based on surveys. Participants were required to complete a questionnaire to report their emotional engagement. The results show the behavioral and cognitive engagement of the experimental group with teacher facilitation were significantly higher than that of the control group without teacher facilitation. However, there was no significant effect of teacher facilitation on participants’ emotional engagement in the experimental group. It is also found that the emerging student leaders show similar positive effects of teacher facilitation towards improving behavioral and cognitive engagement. Future studies about the moderating effects of teaching experience and student leaders are also needed.
•We examined the effects of teacher role on student engagement in online discussion.•Content analysis and questionnaires are used to evaluate student engagement.•Behavioral and cognitive engagement are higher in groups with teacher facilitation.•Teacher's role has no significant impact on student emotional engagement.
The flipped learning model, which “flips” traditional in-class lectures with collaborative activities, has gained many followers and converts in K-12 education. However, a review of previous studies ...shows that the flipped model is still underutilized and underexplored in the higher education context. Research and design models for flipped learning in higher education are also insufficient. This study attempts to fill this gap by developing a model that can provide a foundation for further research and practice for flipped learning in higher education. Building from the four pillars of F-L-I-P™ (Flexible Environments, Learning Culture, Intentional Content, and Professional Educators), the proposed research and design model named “FLIPPED” adds three extra letters—P-E-D (Progressive Activities, Engaging Experiences, and Diversified Platforms)—to the F-L-I-P™ acronym. This model was implemented in a “Holistic Flipped Classroom” environment and evaluated based on a student survey, interviews, and an analysis of computer system logs. Findings demonstrated that the proposed model was effective; students reported that they were satisfied with the course, their attendance improved, and their study efforts increased. Results also suggested that the transactional distance changed during the learning process: highly motivated students performed much better than less motivated students. However, some students retained their former passive learning habits, and this resulted in an obstruction to full adoption. Reflections on the achievements and challenges of the “FLIPPED” model have culminated in various examples, guidelines, and suggestions for practitioners as they consider their own design, implementation, and adoption.1
•Develops the FLIPPED research & design model for higher education.•Uses the Holistic Flipped Classroom approach to implement the model.•Research was conducted using a survey, interviews, and system log analysis.•Identifies challenges and problems during the implementation.•Provides strategies, examples, and guidelines for future practitioners.
Background
Investigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning ...achievement. While the majority of research focuses on the relationship between emotions and interactions in MOOC forum discussions, research on identifying the crucial difference in emotion sequence patterns among different interaction groups remains in its infancy.
Objectives
This research utilizes deep learning and sequential pattern mining to investigate whether there are differences in emotion sequence patterns across different groups of learners who exhibit various types of interactions in online discussion forums.
Methods
Data from a comprehensive array of sources, including log files, discussion texts and scores from 498 learners in online discussion forums, were collected for this study. The agglomerative hierarchical algorithm is used to classify learners into groups with different levels of interactions. Additionally, we implement and evaluate multiple deep learning models for detecting different emotions from online discussions. Relevant emotion sequence patterns were identified using sequence pattern analysis and the identified emotion sequence patterns were compared across different groups with different levels of interactions.
Results and Conclusions
Using an agglomerative hierarchical algorithm, we classified learners into three distinct groups characterized by different levels of interactions: high, average and low level. Leveraging the bi‐directional long short‐term memory model for emotion detection yielded the highest predictive performance, with an impressive F‐measure of 94.01%, a recall rate of 93.83% and an accuracy score of 95.01%. The results also revealed that learners in the low‐level interaction group experienced more emotion transition from boredom to frustration than the other two groups. Therefore, the aggregation of students into groups and the utilization of their MOOC log data offer educators the capability to provide adaptive emotional feedback, customize assessments and offer more personalized attention as needed.
Lay Description
What is currently known about the subject matter
Emotions are dynamic over time when learners experience cognitive disequilibrium/equilibrium.
Online interactions are critical components, which influence learners' emotional state, cognitive processes and learning achievement.
It is not clear what are differences in emotion sequence patterns across the groups with different interaction types.
What the paper adds
An agglomerative hierarchical algorithm was implemented to cluster learners into three groups by analysing behavioural data.
We explore possibilities for automated classification of emotions using deep learning approaches.
Learners in the low‐level interaction group experienced more emotion transition from boredom to frustration.
Implications for practitioners
A considerable amount of effort should be expended to identify and respond to learners who experience boredom and frustration emotions.
Designing interventions or scaffolding to facilitate learners' interaction and promote favourable emotions.
Educators could provide more personalized support based on learners' online interaction cluster.
Robots can benefit education in many ways, and robotics holds great promise as a learning technology. However, the use of robots in education remains largely unknown to both researchers and ...educators. Many educators and developers have questions regarding the essential applications for robots used in education. Thus, this study aims to identify the essential applications of educational robots. To gain insight from the perspectives of researchers, experts and educators, we investigate the requirements of educational robots in all levels of education through 3 approaches: systematic literature review, expert interviews and instructor surveys. We review the extant literature that addresses the use of robots in education and adopted data from one of them, and conduct interviews with industry experts, scholars and instructors. We also conduct an online survey that invites instructors from six different levels of education to respond. We find the following to be the top five essential applications for educational robots: language education, robotics education, teaching assistance, social skill development and special education, and guided learning through feedback.
•An investigation that identifies the essential applications of educational robots.•The investigation consists of a systematic literature review, expert interviews and instructor surveys.•Robotics, language education and robot teaching assistant are the top three applications.•Robotics, language education and social and special education are the core applications.•Preschool and primary school group have greater potential to implement educational robots in near future.
Generative artificial intelligence (GAI) applications, such as ChatGPT (Chat Generative Pre-trained Transformer) and Midjourney, have recently attracted much attention from researchers and school ...teachers. While many people are eager to learn more about GAI applications, some scholars are concerned about the potential misuse of them. It is predicted that the use of GAI applications will increase rapidly in the coming years. Therefore, it is important to consider the challenges and research issues through some concrete application examples of using GAI for education. In this position paper, the authors aim to address these issues from the perspectives of academic research and educational objectives. Along with defining GAI, several illustrative examples of using GAI applications in educational settings are provided. Moreover, potential research issues of GAI-based learning, including research design, relevant learning strategies, research focus, and measuring tools, are discussed. ET＆S journal is especially welcoming research on unlocking the potential of GAI for education to realize the two notions of ＂Knowing why is the essential element for learners to have in-depth understanding＂ and ＂It is all about prompts: Get rid of the 'search' mindset and use 'programming prompt' instead.＂
The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the more pressing the need is for us to understand it. ...This paper addresses research on the use of AI to evaluate new design methods and tools that can be leveraged to advance AI research, education, policy, and practice to improve the human condition. AI has the potential to educate, train, and improve the performance of humans, making them better at their tasks and activities. The use of AI can enhance human welfare in numerous respects, such as through improving the productivity of food, health, water, education, and energy services. However, the misuse of AI due to algorithm bias and a lack of governance could inhibit human rights and result in employment, gender, and racial inequality. We envision that AI can evolve into human-centered AI (HAI), which refers to approaching AI from a human perspective by considering human conditions and contexts. Most current discussions on AI technology focus on how AI can enable human performance. However, we explore AI can also inhibit the human condition and advocate for an in-depth dialog between technology- and humanity-based researchers to improve understanding of HAI from various perspectives.
•Investigation of the effect of gender and personality on perceiving game elements.•The experiment was conducted in a public university with 189 undergraduate students who took three online gamified ...courses, based on the self-determination theory, during two academic years.•Gender and personality can affect students’ perceptions toward different game elements.•Gender moderates the effect of personality on students’ perception of the implemented game elements.
While many studies have reported the effectiveness of gamification in motivating students and making learning more fun, some others have reported contradictory findings regarding the potential of implementing game elements in an online gamified course. It is recognized that designing a successful gamification is a challenging process. Previous studies have shown that students’ individual differences may impact their gamification experiences. This study complements the available body of research by examining the effect of gender and personality differences on students’ perception of gamification in education. An experiment was conducted in a public university with 189 undergraduate students who took three online gamified courses, based on the self-determination theory, during two academic years. The results showed that gender and personality can affect students’ perception of specific game elements. For instance, females are more likely to find feedback useful than males. Additionally, students low in extraversion are more likely to find a progress bar useful than students high in extraversion. The results also showed that gender moderates the effect of personality on students’ perception of the implemented game elements. For instance, males low in extraversion are more likely to perceive badges’ usefulness in gamified courses than males high in extraversion, whereas females low in conscientiousness are more likely to enjoy feedback than females high in conscientiousness. The findings of this study can help designers and educators personalize their gamified courses’ design based on personality and gender.
This commentary responds to Ping Li and Yu-Ju Lan's article "Digital Language Learning (DLL): Insights from Behavior, Cognition, and the Brain" (same journal issue).
Knowledge hiding in higher education Ghani, Usman; Zhai, Xuesong; Spector, J. Michael ...
Higher education,
02/2020, Volume:
79, Issue:
2
Journal Article
Peer reviewed
Recent research has focused on students’ knowledge sharing behavior and its driving factors, which promote the knowledge transmission and exchange in different educational settings. However, little ...research has focused on factors that inhibit knowledge sharing. Based on social learning theory and social cognitive theory, the current study investigates the relationships between knowledge hiding behaviors and students’ belief about interactional justice and professional commitment. Based on 312 reliable questionnaires collected from Chinese and international postgraduate students in China, this study explored postgraduate students. Three knowledge hiding behaviors include playing dumb, evasive hiding, and rationalized hiding, all of which is negatively related to interactional justice. Furthermore, the moderating effect of professional commitment was found to influence evasive hiding more significantly than the other two aspects of knowledge hiding. Based on the findings of the study, implications are elaborated for learners, supervisors, and higher education.
The increased interaction with multimedia content has been recognized as a significant factor to improve learners’ learning outcomes. As a result, interactive video lectures are increasingly being ...adopted in digital learning contexts for increasing interactivity. However, conventional video lecture lacks interactive learning activities (ILAs) that are an indispensable component of interactivity. Interactive video lectures can provide learners opportunities to obtain timely constructive support to produce effective learning outcomes because of ILAs. In order to carefully design and create ILAs for effective learning, instructors need to invest substantial efforts for conceiving the content and interactions of ILAs. This study proposed an approach to design the content of ILAs by leveraging collective intelligence (CI) gathered from the discussion forums specifically related to the content of video lectures. When learners exercised ILAs, meaningful interactions by speaking and gesturing worked as learning support to improve comprehension of learning materials. To evaluate the effects of the CI-based ILAs, an experiment was conducted with 90 university students, who were randomly and equally assigned to three different groups (i.e., embodied interactive, non-embodied interactive, and conventional video lectures). The results show that learners who learned with the embodied interactive video lecture performed better in comprehension and retention of learning contents than the other two counter groups. The findings also revealed that this approach does not impose any additional cognitive load on learners. The design guidelines derived in this study can serve as reference for instructors to create interactive video lectures with CI-based ILAs in digital learning contexts.
•Better comprehension and retention with embodied interactive video lectures (EIVLs).•Richly embodied learning experience with natural user interface technologies.•Interactive learning activities powered by collective intelligence.•Contextual scaffolds based on six scaffolding functions.•The RESUE guidelines on choosing appropriate content for creating EIVLs.
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