University Learning Research Articles

Instructors’ Perceptions of the Suitability of the Current English Assessment System: A Study from the Perspective of Private Universities in Bangladesh

A specific teaching-learning environment frequently governs EFL instruction and learning through assessment. In order to help students improve their four skills of language proficiency and prepare them for a globalized society, English courses are offered at private universities in Bangladesh. Formative and summative assessments are typically used to evaluate students' progress and achievement in the English language throughout their courses. The current study has attempted to investigate the appropriateness of the English assessment system and how it was related to English language curriculums of Bangladeshi private universities. A questionnaire survey was conducted in this quantitative study to collect data from 60 EFL instructors from fifteen private universities in Dhaka. The data was analyzed using both descriptive and inferential statistical methods with the statistical software SPSS 26.0. The study has discovered that the current assessment system is not perfectly aligned with the EFL curriculums and syllabuses, teaching approach, language materials, and classroom activities of English teachers at Bangladesh's private universities. The study has proposed that the current assessment method needs to be modified to guarantee high-quality EFL instruction and learning in Bangladesh's private universities.

A hybrid model combining environmental analysis and machine learning for predicting AI education quality

Due to the evolution of the management system of educational institutions, the need to use artificial intelligence-based tools in the university is felt more and more. Therefore, platforms based on artificial intelligence (AI) are getting more developed day by day. Therefore, in this study, firstly, by dividing the macro environment into three different parts, including external, intermediate, and internal, a set of corrective measures in the university teaching and learning management in the background of AI education is proposed, and also the effects of their application in the management of teaching and learning in universities were discussed. Also, in order to evaluate the quality of AI training programs in higher education, a new approach based on the multilayer perceptron (MLP) algorithm was presented in which the capuchin search algorithm (CapSA) was used to adjust the weight vector of the neural network. According to the results, Proposed and ANN (SCG) models had the best and worst performance respectively in reproducing observations. The results showed that corrective measures in all environments can help the development of AI education in universities. The results of conducting a case study and examining various evaluation indicators showed that the proposed approach in this study has a good accuracy in predicting the target variable (quality of education). The values of CCC, SROCC, PLCC and R2 indices related to the proposed model are equal to 0.9611, 0.9805, 0.9731, and 0.9803, respectively, which are all higher than the corresponding values in other models.

Assessing Digital Media and the Learning Process among Private Universities in Nigeria in the Post-COVID-19 Era

Studies on digital media learning among schools in Nigeria have focused more on the experiences of both learners and facilitators in the COVID-19 lockdown with only a few reflecting on the post-COVID experiences of learning using the digital media. Relying on secondary sources, this study interrogated the fate of digital media learning among private universities in the post-COVID era in Nigeria. The findings revealed that unlike the public universities which struggled to respond to the challenges of learning amid the COVID-19 lockdown, several private universities responded firmly to ensure learning was sustained. This response, however, ushered in new challenges like limited finance, erratic internet services, poor student assessment procedure, lack of basic technology skills among tutors and learners, absence of defined coping mechanisms for coping with the changes and concerns about the integrity of evaluation and assessment in the context of digital learning. This has been made worse in the post-COVID-19 era where tutors and learners have struggled even more to meet the challenges of digital learning. However, the need to sustain digital learning as the new normal at private universities in Nigeria remains consistently compelling given the relative positive impacts of digital learning on students’ performances and teachers’ capacity-building across the globe. This study, therefore, concluded that private universities in Nigeria must ensure that they capitalise on the gains recorded from online learning by sustaining its practice in the post-COVID era to meet global standards. Also, the government must provide an adequate support system to sustain digital learning in universities. Keywords: Covid-19, Education, Digitisation, Learning, Communication.

Efficacy of periodic students’ instructor evaluation to enhance lecturer’s performance: A content analysis

Systematic collection and analysis of data from students on lecturer’s endeavours in the instruction process is one of the crucial ways of getting feedback on lecturer’s performance. This study underscores the critical aspect of utilizing student feedback as a valuable tool for improving instructional practices and enhancing higher education pedagogical experiences for both educators and students. It focuses on the effectiveness of Periodic Students’ Instructor Evaluation (PSIE) at university level aiming at identifying common themes and areas for improvement. Using the content analysis methodology, the study utilized relevant secondary data sourced from Google web search engine for the period 2018 to 2024. Findings showed that PSIE insights are influential assessment of lecturer’s teaching effectiveness and is an important source of information to the lecturers and the administrators and it enhances quality and programme improvement. Furthermore, PSIE encourages the students’ voice and active participation in university affairs through confidential participation, thereby ensuring that the students’ experiences are fore grounded at the university learning and teaching interface. This meaningful input from students can be critical in the success of such teaching evaluation systems. The researchers also realized that PSIE should be coupled with discussions and consultations of the feedback receivers in order to maximise its utility. Researchers concluded that PSIE is not a witch-hunting exercise but, when properly done, it becomes an important tool with positive impact. This study recommends feedback survey tools that can be tailored to the specific pedagogical approaches and university learning outcomes.

Applicability of ADDIE model in Analysis of Content Resources for Blended Learning in Universities: Case of Bachelor of Science External at Makerere University

This article draws from a study on the applicability of the ADDIE model in the Analysis of Content Resources for Blended Learning at Makerere University. The study analyzed the current content resources for Blended Learning (BL) and strategies for improving content resources for BL on Bachelor of Science External at Makerere University. The connectivism theory was used as lens to explain findings in this study. The qualitative research design and interpretivism paradigm were adopted to explore opinions of the respondents for this study. The multi-stage sampling method with purposive technique was used to determine the general student population on the Bachelor of Science External program, identifying and selecting students in their various subject combinations. The first stage was identification of the college ‘College of Natural Sciences’, followed by purposive selection of B.Sc. External program. The selection of a B.Sc. program was followed by identification of various subject combinations and lastly purposive selection of students with consideration of the year of study and their lecturers. Interview data was analyzed in Atlas ti. coding In-vivo to create the quotations and memo of network view for findings of this study. The Atlas ti was used to organize uploaded transcribed interview data to generate the network views that contained meaningful information aligned with the objectives of the study. The analyze phase of ADDIE model involves the determination of instructional goals by understanding the competency gaps through which academic progress is catered for within the intended learning outcomes both in Biological and Physical sciences to address the needs in society. Target audience analysis require taking consideration of; gender, learners’ experiences, learning interests, geographical location, motivation for learning, economic status, language proficiency, learners’ abilities and intimate pattern for inclusivity in BL. Required resources were smart phones and laptops to access the internet-based content resources, artificial intelligence such as Chat GPT for learning, and digital learning using communication tools like; zoom, WhatsApp, google meet and TikTok. The well-designed lecture theatres, smart rooms and multimedia studios facilitate the preparation of content resources fit for BL. Strategies were; understanding of marital status of learners, experiences, motivation to pursue a course, excellent search engines such as refseek.com, worldcat.org, link.springer.com, bioline.org.br among others all important in designing of content resources for BL. Continuous professional development programs for lecturers and orientation of learners with support of a cross cutting course, buy your own device policy at subsidized price from pre-qualified vendors like Stanbic at Makerere University and adoption of MUELE to build effective BL solutions for universities.

Analyzing the Elements of Effective Professional Development in Teacher Education

The purpose of this study is to identify the components of effective professional development in teacher education. Information is analyzed in a descriptive way in this study. A systematic review of papers published in the teaching, teacher professional and teacher education journal identifies key components that contribute to successful teacher education. Teacher education is important dimension for promoting effective professional growth. Effective professional development is identified as crucial for educators to develop the necessary pedagogical skills for teaching field. The elements of teacher education include collaboration, learning experiences, cooperation, deliberation, active learning, modeling; coaching, mentorship and support mechanisms, feedback and reflection, continuous involvement, and a focus on student outcomes are found. These factors enhance instructional effectiveness and ultimately resulting in continued improvements in the quality of teaching. Effective professional development should be regular, context-sensitive, and focus toward improving teaching techniques. Similarly, integrating the use of technology and new teaching practices is very important in contemporary teacher education. Teacher education programs are evaluated and adapted on a continuous basis to meet the changing demands of teachers. The findings indicate that recent trends in professional development for teachers emphasize enhancing teaching skills, classroom management, and subject matter knowledge. The approach to teacher education should be collaborative and collegial learning environment. The future researchers should do research on culturally responsive teaching in teacher education and the effect of understood bias training on teacher attitudes and practices.

English CEFR proficiency with autonomous online learning among Thai tertiary learners in a provincial public university

This study aims to assess the Common European Framework of Reference (CEFR) level English proficiency of tertiary students engaged in online English autonomous learning outside of traditional classroom settings and to identify students' online autonomous learning strategies to enhance CEFR-level English proficiency. An explanatory sequential mixed methods design was employed, involving 129 Thai tertiary students participating in the English development project of Rajamangala University of Technology Lanna (RMUTL), Lampang campus. Data were collected through the Speexx online pretest and posttest program, along with a questionnaire, and were analyzed using descriptive statistics and thematic analysis. The results indicated that after the posttest, overall, 45.75% of all participants achieved a higher CEFR level, 51.94% of all participants maintained their CEFR level, and 2.33% of all participants experienced a decrease in their CEFR level. According to the participants' online autonomous learning approaches, the majority adopted a similar pattern of English learning, which involved assessing English deficiencies, setting personalized learning plans and goals, finding convenient online English learning channels, troubleshooting problems, and monitoring and evaluating the progress.

Quality of e-Learning in Nepalese Universities During the COVID-19 Pandemic

Covid 19 pandemic triggered changes in various socio-economic sectors that were already in pace. Among them, the digitalization of higher education faced one of the profound shifts due to the pandemic. The application of digital education is gaining momentum and is likely to continue until the long future after Covid 19. However, there are limited assessments of its quality, especially in the countries in the global south. The purpose of this study was to assess the quality of online learning in Nepalese universities during the pandemic using a customized SERVQUAL model with six dimensions, namely, tangibility, assurance, responsiveness, empathy, and online-class. The online-class dimension is added in this study, which includes conditions of e-learning such as electricity supply, internet service, electronic devices, time schedule, thus characterizing the conditions in a developing country. Specifically, this study focused students' perceived quality of e-learning in universities in Nepal. A survey was conducted between April to July 2021 among 451 graduates and undergraduates who participated in online classes during the pandemic from six universities to explore their satisfaction with the quality of digital education. The survey included 27 questions related to six dimensions: reliability, responsiveness, assurance, empathy, tangibility, and online-class. The data was analyzed quantitatively using the open-source platform Jamovi (version 2.3.28). The results showed that students expressed satisfaction with the quality of online education of e-learning during the pandemic. However, the study found variations in student perceptions based on demographic factors, highlighting differences in satisfaction levels across diverse student groups. This result implies that despite its increasing application, e-learning needs to accommodate a more inclusive approach to improve its quality and satisfy learners from all socio-economic backgrounds. A blended mode of teaching could be more effective in the changing context. This should be paralleled with instructors as well as support staff training. At the same time, the limitations of online-class can be addressed in collaboration with government agencies and universities which further enhances the quality of e-learning.

In-lecture quizzes improve online learning for university and community college students

Online classes are now integral to higher education, particularly for students at two-year community colleges, who are profoundly underrepresented in experimental research. Here, we provided a rigorous test of using interpolated retrieval practice to enhance learning from an online lecture for both university and community college students (N = 703). We manipulated interpolated activity (participants saw review slides or answered short quiz questions) and onscreen distractions (control, memes, TikTok). Our results showed that interpolated retrieval enhanced online learning for both student groups, but this benefit was moderated by onscreen distractions. Surprisingly, the presence of TikTok videos produced an ironic effect of distraction—it enhanced learning for students in the interpolated review condition, allowing them to perform similarly to students who took the interpolated quizzes. Moreover, we showed in an exploratory analysis that the intervention-induced learning improvements were mediated by a composite measure of engaged learning, thus providing a mechanistic account of our findings. Finally, our data provided preliminary evidence that interpolated retrieval practice might reduce the achievement gap for Black students.

Challenges and solutions to implementing the idea of teacher leadership in teaching and learning in a northern district college in Bangladesh

This study tries to find out the existing scenery of the implementation of teacher leadership in a northern district government college in Bangladesh. Furthermore, the nature of student teacher relationships and the challenges and solutions for implementing the idea of teacher leadership at the undergraduate level is eyed by the researcher. A qualitative approach has been applied in the research where both teachers and students participated in semi structured interviews and focus group discussion. Significantly, Semi-structured interview was taken with five teachers from three faculties namely science, arts and social science, and business studies. Simultaneously, thirty students (15 male and 15 female) from the undergraduate level participated in the focus group discussion separately. A purposive sampling method was used to select participants. Data was analysed after collecting from both teachers and students. Lack of teacher leadership exercise, limited teachers, lack of pedagogical training, excessive pressure of official work, and lack of mental satisfaction in the job are the barriers to implementing the teacher leadership idea. Creation of sound learning atmosphere, use of internal funds, arrangement of training & and seminars, special care for weak students, achieving credibility and trust, professional learning community, teaching award action research activity and mentoring the activities, the idea of teacher leadership can be established at the undergraduate level in the researched college.

Exploring AI-powered personalized learning in universities

The study examines the problem of implementing and developing personalized learning with the help of artificial intelligence (AI) tools in universities. To solve this problem, the following goals were set: identifying the opportunities for personalized learning based on learning tools powered by AI according to university teachers; examining the current state of AI-powered learning technologies using the example of two developing countries; and clarifying the existing challenges and prospects. The study uses a survey and an interview. The results indicate that 82% of the respondents were generally aware of AI and personalized learning. The findings show that 89% of the teachers disagreed with the idea that AI and personalized learning have the potential to replace traditional educational strategies. The teachers identified several challenges, such as the lack of personal communication and difficulties with recognizing emotions, in the proposed strategies. Thus, 43% of the respondents suggested integrating AI into the educational process as a supplement to the traditional teaching methods. The analysis of interviews revealed that 85% of the teachers were positive about the implementation of AI and personalized learning in universities. Nevertheless, educators report that AI-generated tools are still underdeveloped and cannot replace all functions of teachers.

The relationship between cultural awareness and participation in intercultural societal challenge

Student engagement with intercultural societal challenges is investigated for academic programs as the globalizing trend requires students to interact effectively in multicultural settings, yet not all students are capable of doing this. Through explanatory sequential mixed methods, with quantitative data using questionnaires and qualitative data using semi-structured interviews, this study aims to investigate the interplay between cultural awareness and participation in the programs and to findout the challenges. The respondents were seventy-three students who ever followed this program in face-to-face or online meetings. To complete the qualitative data, semi-structured interviews with four students were used. The study revealed that enhanced cultural awareness levels directly influenced student enrollment in intercultural societal challenges. Students involved in the program discussed multiple challenges which included problems with language learning associated with cultural communication issues and differences in understanding between participants. This study both adds to existing academic documents because it demonstrates how cultural understanding drives students to actively participate in international programs, thus demonstrating why formalized cultural competence training should be integrated into university learning. This study brings originality through its examination of experiential intercultural program learning which enhances student adaptability and cross-cultural communication abilities in order to fill knowledge gaps in theoretical cultural awareness research.

Exploring opportunities and pitfalls of nurturing empathy through Virtual Reality in higher education in Norway

New media, such as virtual technologies (VR), are increasingly used with the aim of providing more immersive experiences in learning in schools and universities, including the nurturing of empathy, which is considered important for strengthening democracy, tolerance and social justice. In this article, we explore how students in higher education in Norway understand and interpret empathy in VR immersion. Students at two Norwegian universities watched three examples of VR videos designed to foster empathy, and our analysis is based on focus group discussions with these students. Our analysis is guided by anthropological and sociological conceptualisations of empathy, as well as decolonial perspectives on empathy that emphasise the need for cultural ‘translation’ for empathy to be meaningful across geopolitical contexts. Our results show that the concept empathy is more complex than often recognized by producers of VR content. For empathy to be transformative rather than result in ‘virtual othering’, it is essential to consider the significance of positionality and contextuality of those involved in empathic relations and encounters, rather than assume universality, both in terms of the actors involved (who the empathiser is) and conceptualisations of empathy (what empathy is).

Navigating Remote Learning: A study on Student Perceptions During Times of Uncertainty

Covid 19 pandemic impacted the global economy, operations and people daily lives. The pandemic has a ripple effect on student learning which in turn will impact global economy for years to come. As the Covid-19 pandemic forces educational institutions in several countries to transition from face-to-face instruction to emergency remote teaching, teachers and students face the daunting task of adapting their practices. This research investigated university student attitude toward learning from home while focusing on the Omani context. In addition, it is aspired to understand and analyze different factors affecting student attitudes toward remote learning including demographic factors, technology access, technology competencies, learning preference and technology usefulness. The study was conducted using an online remote learning pedagogy questionnaire designed using a five-point Likert scale. A total of 279 students participated in this study from various study levels. The study findings highlighted important elements that should be addressed by educational institutes when offering a remote learning experience. Firstly, student-teacher engagement is an essential part of remote learning. The ability for students to communicate seamlessly with their facilitator is a critical factor in promoting positive attitudes toward the remote learning process. Secondly, technology accessibility impact student’s perceptions and attitudes toward remote learning. Reliable and seamless access to the university learning platforms and online resources are critical success factor. Finally, offering students a variety of online learning resources activities and interactive materials can impact student satisfaction. Other factors influencing students’ attitudes toward remote learning includes previous online experience, online support services, access to technical support, collaboration, and digital curriculum design.

A Comparative Analysis of Artificial Intelligence Learning and In-Class Learning in Kenyan Universities

This study presents a comparative analysis of Artificial Intelligence (AI) learning and traditional in-class learning in Kenyan universities, focusing on learning outcomes, student engagement, and accessibility. The study was conducted across six universities—three public and three private—and the study involved 424 undergraduate students and 35 instructors, utilizing a mixed-methods approach. Quantitative data from surveys and performance records were complemented by qualitative insights from interviews and focus group discussions. The results revealed that students using AI learning platforms outperformed those in traditional settings, particularly in Science, Technology, Engineering, and Mathematics (STEM) disciplines, with AI learners achieving an average score of 78.4% compared to 72.9% for in-class learners. Engagement levels were also higher among AI learners, who reported more frequent interaction with learning materials and instructors, especially during asynchronous sessions. However, significant challenges were identified in terms of accessibility, especially in rural universities where unstable internet and a lack of digital infrastructure hindered AI adoption. In contrast, in-class learning was more accessible in these regions but faced issues of overcrowded classrooms and limited resources. The study suggests that while AI learning offers significant advantages in terms of personalization and flexibility, a blended learning model combining both AI and in-class methods may provide the most effective educational experience. This model could address the strengths and limitations of each approach, especially in addressing the digital divide and fostering critical thinking. The findings suggest policy reforms to improve digital infrastructure, promote instructor training, and ensure inclusive access to AI-driven education in Kenyan universities.

Liberating learning in the empathetic university

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Kanformer: an attention-enhanced deep learning model for predicting student performance in virtual learning environments

With the rapid growth of digital education, massive open online courses (MOOCs) have gained widespread popularity. However, high failure rates and low completion rates remain challenges, highlighting the need for accurate student performance prediction to identify at-risk students and implement timely interventions. Many existing models assume simplistic or linear dependencies, failing to capture the complex relationships and cross-dimensional interactions between different student features. Therefore, KANFormer model is developed, a novel deep learning (DL) architecture that integrates multi-head self attention (MHSA) mechanisms with Kolmogorov–Arnold networks (KAN) to effectively captures complex patterns within large-scale student datasets, enabling more accurate predictions of student performance. KANFormer processes student demographic, academic, and engagement features from the open university learning analytics dataset (OULAD) while applying a feature elimination process to enhance dataset quality, reduce redundancy, and mitigate multicollinearity, ensuring optimal predictive performance. The model is evaluated on both binary and multi-class classification tasks, distinguishing successful students (“Distinction” & “Pass”) from unsuccessful ones (“Fail” & “Withdrawn”), while also predicting individual performance categories. Ablation experiments analyze the impact of MHSA and KAN on model performance. Experimental results on the OULA dataset, comprising over 29,000 samples, demonstrate that KANFormer outperforms traditional and state-of-the-art models, achieving superior accuracy, precision, and F1-score in student performance prediction. To enhance interpretability, SHAP (SHapley Additive exPlanations) values are utilized to quantify each feature’s contribution to predictions. SHAP analysis shows that academic assessments (exam scores, TMAs, CMAs) are the most influential predictors of student success, while engagement indicators play a secondary role. Furthermore, the model’s generalization ability is validated on additional datasets, including ASSISTments2012, xAPI, and the Mathematics and Portuguese Language Courses dataset, proving its scalability and robustness across different educational contexts. By accurately identifying at-risk students, KANFormer offers a scalable, interpretable, and highly effective solution for improving student retention and success in online education, providing actionable insights for personalized learning interventions.

Research on the Construction and Practice of an AI-Driven Personalized Learning Model for College English

With the rapid development of artificial intelligence technology, the field of education has ushered in new changes and opportunities, and college English teaching is also facing new challenges and higher requirements. This study focuses on the AI-driven personalized college learning model and discusses its construction principles, methods and practical effects. Through the analysis of the current university English learning difficulties, it expounds the AI technology in providing accurate learning resources, intelligent guidance and the advantages of learning path planning, builds the fusion of AI personalized learning mode and verifies in practice the students' learning enthusiasm, learning performance and the effectiveness of autonomous learning ability,which can improve and provide useful reference for college English teaching reform.

EFL Students’ Experience in Distance Learning during COVID-19 from a Multidimensional Transactional Distance Perspective

Emergence learning during the COVID-19 pandemic was a challenging experience for many learners possibly due to the physical and psychological distance. This distance conceptualized as a transactional distance according to Moore’s theory is a critical issue in distance learning. Therefore, the present study attempted to (1) determine Saudi EFL students’ levels of perceived transactional distance in distance learning, (2) measure the impact of the interaction of course structure, dialogue and learner autonomy on Saudi EFL students’ perceived TD in distance learning during the COVID-19 pandemic (3) examine the effect of socio-demographic and environmental factors on their perceived transactional distance. By administering a multidimensional electronic (e-)survey to 189 students joining distance learning in a Saudi public university, the study provided evidence of learners’ high levels of perceived dialogue, structure, and autonomy, along with low levels of perceived transactional distance. The correlation analyses showed that dialogue, structure, and learner autonomy significantly and inversely correlate with learners’ transactional distance. The ANOVA analysis revealed that the interaction of dialogue and course structure significantly impacts the perceived transactional distance. Moreover, learners’ levels of perceived transactional distance varied according to their socio-demographic and environmental factors, specifically age, gender, university discipline/major and number of online courses taken by them. The study provides useful pedagogical implications for technology use in sustaining learners’ online learning experiences in terms of dialogue, structure and autonomy and in reducing their perceived transactional distance. Thus, the study is useful for promotion of the distance learning potentials in higher educational institutions.

A meta-analysis examining AI-assisted L2 learning

Abstract Numerous quantitative studies have investigated how artificial intelligence (AI) impacts the development of second language (L2). While individual studies delve into the effects of AI interventions on L2 learning, a meta-analysis provides a comprehensive evaluation of AI’s effectiveness in second language acquisition (SLA). Despite the growing body of meta-analytical research in AI-assisted language learning, several potential moderators have not been thoroughly investigated in previous meta-analyses. This meta-analysis examines the effectiveness of AI-assisted L2 learning and analyzes factors that can influence the effectiveness. The analysis included 15 studies that involved a total of 2,156 participants and generated 53 effect sizes. After correcting for measurement and sampling error, AI-assisted L2 learning demonstrated a positive large effect with d = 1.167. The Q statistic suggested that the true effect sizes varied significantly across studies, which warranted conducting a theory-based moderator analysis. The results of the moderator analysis revealed that type of AI-assisted interactions was not a significant moderator affecting the effectiveness of AI-assisted L2 learning; AI-assisted L2 learning was more beneficial in developing receptive skills than productive skills; AI technologies excelled at building learners’ vocabulary skills compared to other language skills; the effectiveness of AI-assisted L2 learning was higher in an in-class context than in an out-of-class context; IMALL was more impactful for L2 learning than ICALL; and there was no significant difference in the effectiveness of AI technology intervention for L2 learning between K-12 and college learners.