Learning In Higher Education Research Articles

Technological self-efficacy and mindfulness ability: Key drivers for effective online learning in higher education beyond the COVID-19 era

During the COVID-19 outbreak, Israel’s higher education system swiftly transitioned to emergency-adapted online distance learning. Yet, limited research has assessed effectiveness of online learning (EOL) for Arab students in Israel. This study delves into Arab students’ EOL perceptions, focusing on cognitive and emotional aspects. Using a quantitative method, it explored the link between technological self-efficacy (TS), mindfulness ability (MA), and EOL during the pandemic among students from three Israeli academic institutions (N=378). Results showed a positive association between TS and EOL. Further, MA moderated TS-EOL relationship. Men demonstrated higher TS than women. There were noticeable EOL differences between undergraduate (pre-service teachers) and graduate (in-service teachers) students, with the latter exhibiting an advantage. This research contributes to the evolving discourse on post-pandemic online learning, shedding light on potential gender disparities and highlighting the importance of both TS and MA for successful online learning. The findings have implications for instructional designers, educators, policymakers, and academic programs.

Factors Influencing Undergraduate Students’ Online Learning Outcomes: A Structural Equation Modeling

Online learning is on the rise, and it is becoming an important mode of learning in higher education. However, there are limited models that explain the link between learners’ characteristics, learning processes, and outcomes in an online learning environment. This study aims to examine the influence of online self-regulation on the experiences and engagement of students during online learning processes and their outcomes in terms of satisfaction and academic performance. A quantitative correlational design was used to achieve the objective of this study. Data collection was carried out using an online survey. A total of 609 undergraduates from four public and private universities in Malaysia were sampled. The partial least squares structural equation modeling (PLS-SEM) was run to analyze the obtained data. The results showed that online self-regulation is a presage factor affecting online learning processes. It significantly and positively affects students’ experiences and engagement in an online environment. These three factors explained more than two thirds of the variance in satisfaction, a key learning outcome of online learning. However, its predictive value for academic performance was weaker. The developed model can be used as a framework to promote positive online learning outcomes among undergraduate students. The implications of the study and recommendations for future research are discussed in this paper.

How Do Indonesian Student-Teachers Experience Wellbeing during Research Supervision? A Qualitative Interview Study

Research supervision is a collaborative and interactive process that plays a significant role in shaping the successful outcome of a student's learning in higher education. Crucial, but often neglected, we believe that the role of students’ wellbeing during dialogic and sociocultural interaction such as communication styles and expectations during thesis supervision raises issues concerning best practice in students' success. This qualitative interview study investigated thirteen student-teachers’ wellbeing at one private and one public English school in an additional language university context. Drawing on Seligman's positive psychology and Longo’s wellbeing thematic coding analysis, we reported how professional dialogue from dialogic and sociocultural perspectives in the supervisory interaction shape student-teachers’ five wellbeing dimensions: positive emotion, positive relationship, engagement, meaning and accomplishment. Micro elements of wellbeing were identified and analysed inductively. The findings illuminated how institutional and environmental cultures intricately shape students' wellbeing, highlighting that wellbeing is not only constructed through supervisory advice but also dynamically challenged and nurtured through ongoing dialogic interactions among students. The policy and practical implications arising from this research were discussed.

Evaluating the efficacy of Kahoot as a computer-assisted language learning tool in higher education

This study aimed to analyse the effectiveness of using the computer tool Kahoot for foreign language learning in higher education, as well as to address the problem of students' lack of motivation in learning English. This study hypothesised that using the Kahoot app in foreign language classes would improve students' motivation through its engaging approach. To test this hypothesis, a survey was conducted among students who had studied English using this tool. The results showed that students who used the Kahoot app in the classroom were significantly more motivated to learn English. This was made possible by the app's ability to vary the learning tool depending on the needs of the students, which encouraged their active participation in the learning process. The effectiveness of Kahoot as a tool in higher education for learning foreign languages is promising. Its engaging and interactive format, instant feedback, and collaborative learning opportunities make it a valuable tool for enhancing language learning. However, it is important for teachers to be aware of its limitations and complement it with other activities to ensure comprehensive language development. Overall, Kahoot can be a valuable addition to the language learning toolkit in higher education.

A Bibliometric Review of Studies about the Acceptance of Artificial Intelligence Technologies in Teaching and Learning in Higher Education

The growing incorporation of artificial intelligence (AI) tools in higher education (HE) has led to the use of indicators that allow the real impact of these tools to be identified in the teaching and learning process. In this sense, this study developed a bibliometric review on the acceptance of AI technologies in HE, providing an analysis of indicators on scientific production, with the aim of identifying prevalent thematic areas and knowledge gaps. From a methodological point of view, this study was carried out using a quantitative approach with a descriptive level, utilising 56 publications drawn from the Scopus database. The results show a sustained evolution with a growing trend in scientific production since 2021. The most predominant thematic area is evaluation of the acceptance of AI technologies in HE, making greater use of the Technology Acceptance Model (TAM) and the Unified Acceptance and Use of Technology theory (UTAUT). Therefore, it was concluded that the existing literature shows a sustained interest in investigating the acceptance of AI technologies due to the importance of determining the impact generated by their applications in different contexts or scenarios of the reality of HE in regard to the extent that AI technology is developed. This is because, on some occasions, its application does not necessarily lead to meeting the expectations raised in the teaching and learning processes. Finally, the gaps that need to be addressed in future research are "cultural and contextual diversity in AI acceptance", "emerging models of AI acceptance", and "critical elements influencing the acceptance of AI technologies", in HE.

Flipped classroom: bibliometric analysis and contributions of teaching and learning in higher education between 2019 and 2023

Flipped classroom: bibliometric analysis and contributions of teaching and learning in higher education between 2019 and 2023

Effectiveness of the Cooperative Learning Method in English Subjects in Higher Education

Background. Cooperative learning methods have become the main focus in efforts to improve learning outcomes at the tertiary level, especially in English subjects. Its use is believed to provide a more dynamic and interactive learning environment, enabling students to develop English language skills holistically. Purpose. This research aims to explore the effectiveness of cooperative learning methods in English language subjects at universities, with a focus on speaking, listening, and understand English. Method. This research uses a quantitative approach with an experimental research design. Two groups of students consisting of the experimental group (applying the cooperative learning method) and the control group (applying the conventional method) were given speaking, listening and understanding English ability tests before and after the learning intervention. Results. Data analysis showed that the cooperative learning method had a significant improve students' speaking, listening and understanding English skills in higher education. The average score of the experimental group on tests of speaking, listening and understanding English was significantly higher than that of the control group. Conclusion. In the context of English subjects in higher education, cooperative learning methods have proven to be effective in improving student learning outcomes. Their use can enrich learning experiences, increase social interactions, and prepare students for success in an increasingly connected global environment. It is hoped that further support and application of this method can strengthen the quality of English language learning in higher education.

Exploring Generative AI Tools Frequency: Impacts on Attitude, Satisfaction, and Competency in Achieving Higher Education Learning Goals

The urgency of using AI in the educational environment as a medium for optimizing student learning personalization in synchronous and asynchronous learning needs to be done to ensure that students experience improved attitudes, motivation, and learning satisfaction, which is reflected in student competencies. This study aimed to investigate the impact of ChatGPT use frequency on students' attitudes, satisfaction levels, and competence in higher education learning goals. The type of research employed was non-experimental quantitative research, specifically ex-post facto research. The sample size was 257 people, which was determined using the criteria of Issac and Michael (1971) and sampling distribution with a proportional random sampling technique. Data collection techniques that utilize questionnaires are effective in obtaining quantitative data. The PLS SEM analysis was used to determine the effect of exogenous latent variables on endogenous latent variables. The study revealed that reliability and satisfaction with ChatGPT positively impact learning outcomes, while the perceived impact on competence does not significantly enhance learning objectives. Increased usage frequency moderates these effects, diminishing the positive influence of perceptions, reliability, and satisfaction and shifting the impact on competence to negative and insignificant. These findings highlight the complexity of integrating ChatGPT into education, suggesting that initial positive perceptions may not be sustained with frequent use.

Reconnecting and Reconstructing: Taking Stock of the STLHE 2022 conference

Academic conferences are key spaces of knowledge sharing and professional development. As a national society which has pledged to undergo “a robust examination of its attention to equity, diversity, and inclusion throughout the organization” (STLHE/SAPES 2021), we undertook an evaluation of the diversity of participants and presentation topics at the 2022 conference. This paper reports on some of the distinctive aspects of the 2022 conference as well as the findings of the evaluation such as diversity of participants by gender and ethnic identity, province, institution type, discipline, and more. While the 2022 conference was unique in many ways due to being STLHE’s first face-to-face conference since 2019, this report provides a useful baseline with which to compare the diversity of participants and presentation topics in future years. The report concludes with additional recommendations for programming decisions to support and evaluate equity, diversity, and inclusivity at future conferences. Keywords: conference, diversity, participation, presentation topics, Society for Teaching and Learning in Higher Education

Reimagining higher education learning spaces in a new social contract for education

Reimagining higher education learning spaces in a new social contract for education

A model of effective teaching of economics in higher education: the integral role of classroom learning environment, instructional practices, and teacher characteristics

Yidana and Darkwa explore university students’ perceptions of how the classroom learning environment and Economics teachers’ instructional practices promote learning in higher education. The researchers developed and empirically tested a framework that characterises quality Economics instruction. The outcomes of the study suggest that the conceptual framework is valid and reliable and can effectively be used to evaluate quality Economics instruction in higher education. The results indicate key areas that are beneficial concerning student perceptions of teacher effectiveness in promoting student learning. These include planning and preparation, scaffolding of instruction, classroom communication, classroom management, classroom learning environment, and teacher characteristics.

Using machine learning for continuous updating of meta-analysis in educational context

Machine learning and learning analytics are powerful tools that not only support researchers in the detailed measurement and enhancement of learning processes in various learning environments, but also enable the aggregation and synthesis of evidence regarding effective educational practices. This paper describes the development and application of machine learning algorithms aimed at semi-automatic selection of abstracts for a meta-analysis on the effects of simulation-based learning in higher education. The goal was to reduce the workload while also maintaining the transparency and objectivity of the selection process. The algorithms were trained, validated, and tested on a set of 3187 studies on simulation-based learning found in medical and educational databases collected before April 2018. Subsequently, they were utilized to classify abstracts for a follow-up meta-analysis consisting of 2373 studies (published between 2018 and 2020). The aim of training the algorithms was to predict studies’ abstract eligibility based on words and combinations of words used in these abstracts. The application of the algorithms reduced the number of studies that had to be manually screened from 2373 to 711. A total of 458 studies from automatically selected abstracts were included in the full-text screening, indicating the high precision of the algorithms (also compared to the performance of human raters). We conclude that machine learning algorithms can be trained and used to classify abstracts for their eligibility, significantly reducing the workload for the researchers without diminishing objectivity and quality when updating systematic literature reviews with or without a meta-analysis.

Positive Pedagogies: Co-creation partnerships to support social and emotional learning in higher education

Student mental wellbeing is an area of concern within higher education, and there is increasing recognition that social and emotional learning has a role to play in supporting student mental wellbeing. This is particularly the case for mature and part time students, who, as outsiders in many traditional university settings, can experience feelings of isolation and a negative impact on wellbeing. This paper presents a study in UK higher education which applied humanisation and social and emotional learning as a theoretical framework to accompany a co-creation methodology, in order to develop resources to support the wellbeing of mature and part time students. This resulted in co-created resources for students and practitioners aiming to support Positive Pedagogies in higher education. Evaluation of the resources found the project had a two-fold effect on mature and part time student wellbeing and experience; it positively impacted on student co-creators’ SEL and overall experience and on student users as they associated an emotional connection with the resource because it had been developed by fellow students, which provided them with a greater sense of agency due to the voice given to the co-creators. Similarly, practitioners found that the guidance embedded wellbeing into teaching practice and encouraged the development of holistic skills such as emotional intelligence and resilience. This paper therefore concludes that the embedding of the humanising framework into co-creation practices can work to positively enhance the resultant emotional response and therefore positive wellbeing of students in a higher education setting.

Utilization of Artificial Intelligent (AI) in Teaching and Learning in Higher Education for Global Best Practices

The study examined the utilization of artificial intelligent (AI) in teaching and learning in higher education for global best practices. Two null hypotheses were formulated to guide the study. The study adopted the ex-post facto research design. The population of the study consisted of 3800 students and staff of the University within Calabar campus. The stratified random sampling technique was used to select 208 students and staff from a population of 3800 using proportionality of 1.12% with students and staff as basis of stratification from the population. The sample of this study was two hundred and eight (208) respondents. The questionnaire was designed to measure the two sub-independent variables. Mean and standard deviation were used to answer the request questions. While Simple linear regression analysis statistical tool was employed to test the null hypotheses that were formulated to guild the study at 0.05 level of significance. The results of this study shows that there is a significant influence of utilization of artificial intelligent (AI) in teaching and learning in higher education for global best practices and the use of artificial intelligent (AI) in teaching and learning in higher education significantly predict staff and students performance in the university. The study concludes that, there is a significant influence of utilization of artificial intelligent (AI) in teaching and learning in higher education for global best practices and the use of artificial intelligent (AI) in teaching and learning in higher education significantly predict staff and students performance in the university. Based on the conclusion, it was recommended that since there is a significant influence of utilization of artificial intelligent (AI) in teaching and learning in higher education for global best practices, university staff and students should always make use of AI and the university management should put in place AI facilities to increase staff and students .....

Instructional Design Models for Digital Learning in Higher Education — A Scoping Review

Instructional design (ID) is a systematic process that is used to develop education and training programmes in a consistent and reliable way. A key challenge faced by educators is selecting an ID model by deciding which ID model will be more suitable in order to achieve an effective digital teaching and learning process. The objective of this scoping review was to present recommendations to select ID models for digital learning in higher education. Nine databases were searched for eligible publications. The search retrieved 643 records. Forty articles were included in this review. Results show that employing a systematic process in instructional design (an ID model) has produced an effective, consistent and reliable digital teaching and learning process in higher education. Selection of an ID model depends on the requirements of the course, timeline, resources available for the design and development of the course and the expertise in the ID process.

Generative AI: is it a paradigm shift for higher education?

ABSTRACT In this special issue, we explore the opportunities and challenges of using Generative AI (GenAI), in particular, text generators in higher education learning and teaching. As GenAI has become increasingly popular with many staff and students, this special issue provides an overview of the current state of the field and offers insights into future research. This introduction paper consists of four parts. It begins by providing an overview of AI and Generative AI, identifying the gap and framing the special issue relating to the gaps. The second part explores the opportunities and challenges of GenAI in higher education, as identified in the literature. The third part provides an overview of the papers included in the special issue. The final part is the self-reflection of the lead author. The special issue aims to serve as a valuable resource for higher education stakeholders, such as students, practitioners, researchers and managers. We hope this collection will help advance knowledge and future research, encourage innovation and inform evidence-based policy and practices in the field of Generative AI in higher education.

The Attractiveness of Blended Learning in the 21st Era in Higher Education

The face-to-face learning process in the classroom has lost its appeal in this 21st era. The development of technology in the era of the industrial revolution 4.0 requires an educator to innovate in packaging the teaching and learning process to be more efficient and interesting. The learning process must prioritize skills. Learning in higher education is required to emphasize aspects of creativity and innovation, critical thinking and problem solving, communication and collaboration. Learning in higher education requires lecturers and students to always be open and invite students to do technology-based problem solving. This writing uses the literature review method. Literature review contains reviews, summaries, and written thoughts about several library sources (can be articles, books, slides, information, internet, etc.), about the topics discussed. Learning must be creative and innovative. Blended Learning can be an effective alternative and strategy, especially in universities. A learning innovation with the main goal of providing opportunities for various characteristics so that an independent, sustainable, lifelong learning process occurs so that learning will be effective, more efficient, interesting and fun.

The Implementation of Digital Entrepreneurship Learning in Higher Education: A Systematic Literature Review

The phenomenon and topic of digital entrepreneurship learning remain popular for analysis in the era of digitization. Previous literature reviews discussed the context and concept of digital entrepreneurship only. This article presents a literature review aimed at facilitating the search for research publications on digital entrepreneurship, in order to understand the track record, learning models, the impact of implementing digital entrepreneurship learning, and future research opportunities. The article has used the Systematic Literature Review (SLR) method using the Scopus database. This article has used "digital entrepreneurship" keyword for search the resources in January 2014 to May 2023 period. The result of the literature screening process yielded 19 relevant journals. The results of the literature review indicate that research on this topic has shown a significant increase and has the potential to continue to grow. Digital entrepreneurship learning can be designed using experiential learning approaches or the Internet of Things (IoT) and technology approaches. The implementation gives an impact on business opportunities, entrepreneurial intention, innovation, entrepreneurial attitude, and student problem-solving. This study contributes to providing an overview of learning models that can be replicated or modified in future research.
 Highlights:
 
 Growing research on digital entrepreneurship education; impactful learning approaches identified.
 Experiential and IoT-based methods enhance digital entrepreneurship learning outcomes.
 Positive impact on business opportunities, innovation, and entrepreneurial skills observed.
 
 Keywords: Digital Entrepreneurship, Entrepreneurship Learning, Learning Model

Experiential learning in higher education: Assessing the role of business simulations in shaping student attitudes towards sustainability

Education for sustainable development requires innovative pedagogies that entice students' engagement, ensure effective learning outcomes, and develop students' attitudes towards sustainability. The study aimed to investigate the factors that shape students' learning outcomes of a business simulation that incorporates sustainability-related decisions and to examine the effect of business simulation learning outcomes on their satisfaction, intention to continue using business simulations as a learning tool, and attitudes towards sustainability. A survey was distributed at the end of a business simulation course for undergraduates in a private university in the New Administrative Capital in Cairo, Egypt. A total of 262 students participated in the study. The results supported all research hypotheses except for the relationship between learning outcomes and student satisfaction. The study extends our understanding of the use of experiential learning in shaping student attitudes towards sustainability. The future of implementing more business simulations in curriculums depends on students' willingness to continue using such experiential learning tools in the future along with the power of positive word of mouth associated with their experience. The study offers several important implications for higher education institutions leaders and educators.

Empowering collaborative knowledge construction through the implementation of a collaborative argument map tool

Collaborative knowledge construction has been used in higher education to support student groups' collaborative learning activities through students' exchange, negotiation, and reflection of perspectives through peer communications. To support this process, collaborative learning analytics tools have been designed to collect and analyze collaborative process and performance data with a goal to provide actionable feedback and improve learning quality. However, few tools have demonstrated the mechanism and details about how students develop their perspectives during the collaborative knowledge construction process in higher education. To fill this gap, this research proposed a tool named Collaborative Argument Map (CAM) that creatively visualized different types of perspectives students proposed from the individual, peer, and group levels. This tool was further implemented in a graduate-level course in online collaborative writing activities in China's higher education, with a goal to support students' knowledge construction with peers. The summative and process-oriented learning analytics approaches were conducted to reveal the effects of CAM on students' collaborative perceptions, processes, and final products. Results showed that most students made substantive use of the CAM tool and reported positive perceptions of the tool. Further examinations verified the tool's positive effects on improving the students' cognitive engagement levels and the quality of their final collaborative writing products. This research provided practical implications for future CLA tool design and instructional implications for using this type of tool in collaborative learning in higher education.