Abstract

Professional vision (PV), which includes the ability to perceive and interpret classroom events, is important for classroom behaviour management, particularly in the training of student teachers. However, to date, few assessment systems that can assess student teachers’ professional vision by immersing them in a realistic classroom environment (presence) from their own perspective (first-person perspective) have been developed. To address this gap, this study employs a design-based research approach to develop an immersive virtual reality (IVR) technology-based professional vision assessment system (IVR-based PVAS). The research outlines the four-stage process through which the system was constructed to meet the requirements of professional vision assessment and address the core challenges of the current assessment approach. Twenty-four student teachers were enrolled as participants in an implementation case study in which their perceptions and interpretations of seven classroom events related to student misbehaviour were assessed via an IVR-based PVAS. The participants’ problems with perception and interpretation were diagnosed with multiple assessment indices. The benefits of the IVR-based PVAS are summarised, and recommendations are provided for the use of this assessment system in both instruction and research.

Abstract

The Flipped Classroom Model (FCM) has gained widespread acceptance in higher education as an effective pedagogical strategy. Despite its success, the FCM still faces persistent concerns, including a lack of personalized interaction, limited application to introductory courses, and insufficient analysis of the learning process. The integration of generative artificial intelligence (AI) with the Metaverse may significantly enrich and revitalize FCM by introducing affordances that support personalized and immersive learning experiences. This study examined the potential of FCM enhanced by an AI-Generated Metaverse (AIGM) to improve learning outcomes in higher education. Through introducing an AI-powered virtual tutor, this study aimed to address existing concerns and shed light on the nature of knowledge-building interactions between students and the AI tutor. The study involved 94 university graduates who participated in an experimental FCM session. Data on students’ learning performance were collected through pre/posttests, pre-class video engagement, and analyzed dialogic interactions between students and the AI virtual tutor. The findings indicated that the AIGM-FCM positively influenced learners’ academic performance. Interaction patterns revealed that students predominantly engaged with the AI virtual tutor at the onset and at the end of their learning sessions, primarily for the purpose of asking questions. However, the educational dialogues suggested that the depth of collaborative knowledge construction between students and the AI tutor remained at a basic level. Notably, students with lower academic performance were more likely to initiate inquiry-based dialogues with the AI tutor compared to their higher-achieving peers exhibiting a more focusing-oriented approach. In light of these discussions, the implications of this study are also deliberated.

Abstract

Digital game-based learning (DGBL) has emerged as an effective strategy to enhance students’ learning effectiveness. Concept mapping, recognized as a valuable tool for knowledge construction, has been widely implemented in educational settings. However, research indicates challenges when integrating concept maps into games, particularly when the learning content is extensive or complex. In such cases, the use of concept maps may increase cognitive load and negatively impact learning effectiveness. Consequently, this study addressed the issues of reducing learners’ cognitive load and increasing their motivation within a DGBL context. To address these issues, this study developed a progressive concept map-based game approach. This approach integrates mathematical concepts with real-life situations and employs progressive concept maps to facilitate knowledge construction. The aim is to help students understand the relationships between mathematical concepts and guide them in solving learning tasks and achieving higher learning outcomes during gameplay. A quasi-experimental design was employed, with the experimental group using the progressive concept map-based game approach and the control group using a conventional concept map-based game approach. The results indicated that the experimental group significantly outperformed the control group in learning achievement, learning motivation, problem-solving tendencies, and self-efficacy. These findings offer valuable insights for future DGBL research, particularly in the development of progressive strategies.

Abstract

In higher education, concept mapping has been extensively adopted as an assessment and evaluation tool for conceptual knowledge. In recent years, an increasing number of researchers have applied concept mapping to teaching in technology-based environments. However, no research was found to holistically explore the role of concept maps in technological contexts of higher education, or their influences on different learning aspects. To address this gap, the present study referred to the technology-based learning model to conduct a systematic review of the dimensions of the research trends, features (including data analysis, participants, media of concept maps, application domains, and roles of concept maps), and research foci. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedure, this study reviewed the Social Sciences Citation Index articles published in technology-based learning and higher education journals by the end of 2021 from the Web of Science database. The results indicated that in studies on the use of concept maps in technological contexts in higher education (CMHE), the most productive countries/regions (of the first authors) were Taiwan and the United States. The most frequently adopted data analysis methods were quantitative analysis and mixed. In the aspect of application domains, Engineering (including Computer courses) was the most frequent subject for CMHE studies, followed by Language, Social Studies, and Health, Medical and Physical Education. Desk-top computers were the most frequently adopted medium for working on concept maps, although there was increasing adoption of mobile devices. The role of concept maps was mainly as a personal mindtool and assessment and evaluation tool, followed by a collaborative mindtool. In terms of research foci, the cognition dimension was explored the most in CMHE studies, followed by the affect dimension, and learning behavior. Based on the findings of this study, several recommendations are made as a reference for educators, researchers, and policy makers in higher education.

Abstract

Mastering correct pronunciation is crucial for effective Spanish language learning and communication. However, many existing language learning resources lack adequate coverage of pronunciation guidance and feedback mechanisms. This study aims to address this gap by developing SpanishBot, an innovative chatbot designed to facilitate the learning of Spanish letter pronunciations and their variations for non-native speakers. Forty-eight participants, unfamiliar with Spanish, were randomly divided into an experimental group using SpanishBot and a control group using independent learning methods. The experimental group interacted with SpanishBot over a 2-month period, which utilized Python programming and the Line instant messaging platform for delivering personalized pronunciation feedback and practice. Instruments included a pre-test and a post-test, as well as the SpanishBot system, which provided real-time feedback on pronunciation accuracy based on audio recordings. Findings revealed a statistically significant difference between the two groups’ post-test scores, with the experimental group demonstrating remarkable improvements in Spanish pronunciation proficiency. This study highlights SpanishBot’s potential in language education, specifically for Spanish pronunciation training.

Abstract

This study explores the roles of students’ help-seeking profiles when seeking help from AI chatbots, specifically ChatGPT, in a digital game-based learning environment, Summon of Magicrystal. The study involved 102 middle school students who played an online game with the provision of ChatGPT and sought help from ChatGPT while solving physics problems. The results revealed that students’ help-seeking profiles, help-seeking threats, help-seeking avoidance, and instrumental help-seeking were positively correlated. Students’ instrumental help-seeking profile has a positive effect on game performance/engagement, while students’ avoidance help-seeking profile has a positive effect on the number of game attempts. The findings highlight the importance of students’ help-seeking profiles when considering designing AI-assisted game-based learning environments to better support students’ science learning.

Abstract

Collaboration scripts are widely employed in online collaborative learning to enhance student engagement and facilitate collaboration. However, the optimal level of scripting remains a subject of debate. This study aims to address this issue by designing and developing different types of collaborative scripts implemented through conversational agents and supported by WeChat. Utilizing interventional studies, we investigate the effects of these different collaboration scripts on student engagement during online collaborative learning. A total of 54 college students participates in the study, divided into six adaptable scripts teams, six maximal script team, and six minimal script team, with each team consisting of three students. Both quantitative and qualitative data are collected and meticulously analyzed. The results reveal that the maximal collaboration script significantly enhances cognitive interactions, whereas the minimal collaboration script fosters high-quality cognitive engagement. In terms of socio-emotional engagement, the adaptable collaboration script effectively promotes positive socio-emotional engagement, while the maximal collaboration script facilitates greater socio-emotional interactions. Furthermore, thematic analysis demonstrates that all three types of collaboration scripts support student engagement by providing time reminders, facilitating planning, clarifying ideas, and promoting task reflection. These findings have important implications for improving group learning engagement in online collaborative learning environments.

Abstract

This study evaluates the impact of a computer-assisted language learning (CALL) tool for the acquisition of academic English and oral language skills in children learning English as a second language. Using a quasi-experimental design, we compare English proficiency scores for K-5 students who did or did not use the program during the 2020–21 school year. Analyses showed that learners who used the program scored higher on the overall test, including on the oral and written domains, compared to students who did not use the program. When controlling for prior year achievement, we found small, positive but non-significant effects for program users. Proficiency analyses did not reveal any significant differences between student groups. The results show promising evidence that CALL tools, and particularly those focused on oral language development, can be used to provide structured support to students for learning academic English and developing greater overall English language proficiency.

Abstract

To help young students succeed in problem-based learning (PBL), researchers suggested investigating students’ need for cognition (NFC), one’s inclination to exert mental effort during learning. How one puts mental effort into a learning task is related to motivation. If motivated, students are more likely to engage in challenging tasks, put in more effort, and feel competent about what they are doing. This study investigated, through a mixed-methods design, middle school students’ NFC, their motivation to learn, their learning performance, and the relationships among these factors as they engaged in technology-enriched PBL. The findings showed that NFC played an important role in students’ learning. There were significant positive relationships among NFC level, intrinsic motivation both in general and in using PBL, and their learning performance. There were also significant differences in learning performance among the high, medium, and low NFC groups, showing that students in the high group performed better than their counterparts. However, the low NFC group gained more knowledge than their counterparts after using PBL. The study’s findings were further substantiated by qualitative data, which provided nuanced insights that complement the quantitative evidence through detailed topic and sentiment analyses. Limitations of the study were also discussed.

Abstract

This study investigated the sentiment of Twitter discourse on Open Educational Resources (OER). We collected 124,126 tweets containing hashtags related to OER posted from January 2017 to December 2021. We performed fine-grained sentiment analysis using Bidirectional Encoder Representations from Transformers (BERT) to categorize tweets into five sentiment classes: strongly negative, weakly negative, neutral, weakly positive and strongly positive. In addition, thematic analysis was performed by using PyTorch to identify the hidden themes in the tweets. Findings from this study reveal a predominantly positive sentiment toward OER on Twitter, highlighting the perceived benefits of accessibility, inclusivity, and the potential for enhancing educational equality. However, we also found that there are some negative sentiments expressed towards OER, with concerns about quality and effectiveness being the main reasons for criticism. In addition, longer tweets were more likely to express negative sentiments about OER. Finally, the thematic analysis revealed that most tweets center on resources or products that are obtainable through open licensing. These findings have implications for the promotion and implementation of OER and for understanding the role of social media in shaping discourse on education.

Abstract

Argumentation is fundamental and essential in mathematics education. It promotes deep mathematical understanding and helps students connect abstract ideas logically. However, teachers and students in the traditional teacher-centered classroom face difficulties teaching and learning math argumentation. In this article, a cooperative conversation-based tutoring system to foster argumentative skills in learning Pythagorean Theorem was designed. The study analyzed results from an experiment involving 118 middle school students in Taiwan who engaged in formulating, validating, generalizing, and justifying learned mathematics concepts with the help of virtual tutors and peers. The control group included 82 middle schoolers who received the same learning content in the teacher-centered classroom setting. Results confirmed that this agent system promoted significantly better learning outcomes, and the learners’ argumentative experience was also enhanced. Moreover, the unique interactions that took place between each student and the agent system resulted in numerous opportunities for learning mathematical argumentation through an adaptive learning mode.

Abstract

This study explores factors that influence teachers’ technology adoption in technology-rich classrooms and how they interact by integrating task technology fit into the Technology Acceptance Model (TAM). A proposed model was tested via 343 survey responses from Grade 1–12 teachers using structural equation modeling. The results indicated technology task fit played an essential role in teachers’ technology adoption in technology-rich classrooms. Perceived ease of use, however, did not influence teachers’ intention to use technology as previously predicted. The findings suggest that to promote technology adoption in teaching, it is important to help teachers integrate technology into their instructional design and create a supportive culture with sufficient technological support.

Abstract

Role-play tasks have long been used by researchers and practitioners to observe L2 (Second language) speaking performance. This social-situated simulation allows students to employ their language skills to converse about real-life themes. While role-plays are highly plausible to actively engage students in interactive learning environments, it has been challenging to determine whether students perform at an adequate level of speaking proficiency with an appropriate learning approach. Nevertheless, the emerging technology-supported role-play tasks employing AI (artificial intelligence) could enhance competencies, enabling students to perform better in role-plays. Therefore, to enhance and support students’ performance in role-plays, English speaking skills, and their interaction mindset, we integrated an AI-based speaking learning app in the Generalization, Formulation, Correction, Appreciation (AI-GFCA) learning model. A quasi-experiment was conducted in a university with a total of 45 students. One class was randomly assigned to apply the AI-GFCA learning model as the experimental group, while the other was the control group (AI-C). The findings indicated that the AI-GFCA learning model could significantly enhance students’ role-play performance, English speaking skills, and interaction mindset. Furthermore, students produced fewer L2 errors and perceived a better learning experience than the AI-C group. It is noted that with the support of AI learning speaking through role-play tasks, students received sufficient corrective feedback, which encouraged them to establish a positive and motivating learning interaction, thus benefiting their academic performance.

Abstract

The ever-changing global educational landscape, coupled with the advancement of Web3, is seeing rapid changes in the ways pedagogical artificially intelligent conversational agents are being developed and used to advance teaching and learning in higher education. Given the rapidly evolving research landscape, there is a need to establish what the current state of the art is in terms of the pedagogical applications and technological functions of these conversational agents and to identify the key existing research gaps, and future research directions, in the field. A literature survey of the state of the art of pedagogical AI conversational agents in higher education was conducted. The resulting literature sample (n = 92) was analysed using thematic template analysis, the results of which were used to develop a conceptual framework of pedagogical conversational agents in higher education. Furthermore, a survey of the state of the art was then presented as a function of the framework. The conceptual framework proposes that pedagogical AI conversational agents can primarily be considered in terms of their pedagogical applications and their pedagogical purposes, which include pastoral, instructional and cognitive, and are further considered in terms of mode of study and intent. The technological functions of the agents are also considered in terms of embodiment (embodied/disembodied) and functional type and features. This research proposes that there are numerous opportunities for future research, such as, the use of conversational agents for enhancing assessment, reflective practice and to support more effective administration and management practice. In terms of technological functions, future research would benefit from focusing on enhancing the level of personalisation and media richness of interaction that can be achieved by AI conversational agents.