Evaluating Microsoft Teams, Blackboard, Canvas, and Zoom for Online Teaching Effectiveness: A Multi-Dimensional Comparative Study in Higher Education

Tariq; Tassawar

doi:https://doi.org/10.54216/IJAIET.050105

International Journal of Neutrosophic Science | 2024

A Neutrosophic multi-criteria approach for implementing technology in education

The COVID-19 epidemic has greatly expedited the utilization of technology in the realm of education, resulting in the extensive implementation of totally online teaching approaches. These approaches have undergone thorough analysis in various scholarly articles in recent years. This study applies theories of technology acceptance and use in the educational process, employing Neutrosophic analysis to assess criteria for technology utilization in education. The study commenced by formulating an equation to investigate the patterns of technology uptake and use between 2010 and 2024. Additionally, a comprehensive evaluation of the latest literature since 2000 was conducted to identify prevailing trends. The findings suggest that usage plays a vital role in the Technology Acceptance Model (TAM), and structural equations are used as a method to measure it. Neutrosophic analysis provides a thorough and sophisticated viewpoint on the integration of technology in education, emphasizing both the accomplishments made and the obstacles that still exist in this developing area.

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Darío Díaz Muñoz mail -

Patricia Hernández Medina mail -

Saziye Yaman mail

link https://doi.org/10.54216/IJNS.240418

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Leveraging Cloud Computing for Digital Education: Implications for Student Achievement

The research evaluates the effects, which cloud computing and digital educational methods have on scholarly performance. The research used descriptive statistics combined with t-tests alongside ANOVA and regression analysis for interpreting the data findings. The collected data shows students use cloud computing moderately and employ digital education extensively although their educational outcomes stay average. Cloud computing usage exhibited similar levels of acceptance between male and female students however, students from arts streams programs demonstrated increased interest. Cloud computing usage along with digital education experienced superior adoption rates among students residing in rural areas than students settled in urban areas. Research data showed a major statistical linkage between digital education and the levels of academic performance. The educational institution types together with parental work status shaped student interaction with digital educational resources. The study's findings highlight the significant roles played by cloud computing and online learning in raising students' academic performance. The research implies that mixing technology with current education practices will boost educational results while demonstrating why digital competence stands vital in present-day education systems. Academic achievement rates improved in direct proportion to the amount of digital education use by students alongside the fact that private institution students demonstrated higher application of cloud computing platforms and female students demonstrated superior academic outcomes when compared to male students. Numerous students adopt both cloud computing systems and digital education methods because such technology usage is prevalent at accuracy 91.4% of the total students. Out of all the analyses done in the research, the overall F1-score is 92.5, and the fault tolerance of 93.8%.

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Nasser El-Kanj mail -

Chadi El Nar mail -

Marina Abdurashidova mail

link https://doi.org/10.54216/JISIoT.160223

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2026

From Signals to Action: Explainable AI for Engagement-Responsive Instructional Support in Digital Higher Education

Artificial intelligence is increasingly used to monitor learning processes in higher education; however, many analytics pipelines still terminate at prediction and provide limited support for instructional action. The research establishes an explainable artificial intelligence framework which utilizes digital learning environment behavioral data and contextual information to create customized instructional support solutions. The analysis uses xAPIEdu-Data dataset which contains 480 records to build engagement index and create support profiles and predict multiclass performance through rule based action allocation. The study tests three classification models using stratified cross validation. The study selects Random Forest as the most effective system because it delivers superior results across all tests. The selected model demonstrates 0.8021 accuracy and 0.8204 macro precision and 0.8010 macro recall and 0.8084 macro F1 score and 0.9140 macro area under the curve on the hold-out sample. The analysis shows that student absence and composite engagement index andgender and student guardian relationship and support profile and digital resource access arethe most important factors that determine student performance. The final decision layer manages student assignment to instructional support plans which contain attendance-first intervention and adaptive engagement support and family-engagement reinforcement and structured progression coaching and challenge-and-extend pathways. The study develops an analytical framework which connects explainable artificial intelligence to digital higher education instructional decision support systems.

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Andino Maseleno mail -

Meinhaj Hussain mail -

Aygul Z. Ibatova mail

link https://doi.org/10.54216/IJAIET.050101

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2026

Evaluating Microsoft Teams, Blackboard, Canvas, and Zoom for Online Teaching Effectiveness: A Multi-Dimensional Comparative Study in Higher Education

The rapid institutionalisation of online and hybrid delivery models in higher education has left instructors and academic administrators managing a fragmented landscape of dedicated learning management systems, video conferencing platforms, and collaborative productivity suites that overlap substantially in function but differ markedly in pedagogical affordance. Selecting a platform or combination of platforms is consequential for instructor workload, student engagement, and learning outcomes, yet the evidence base for such decisions remains limited to narrow singleplatform evaluations or anecdotal comparisons. This paper presents a systematic multi-dimensional comparative evaluation of four widely adopted platforms—Microsoft Teams, Blackboard, Canvas, and Zoom—drawing on original survey data from 284 instructors and 642 students across five higher education institutions. Nine evaluation dimensions are examined: content delivery, real-time collaboration, assessment and feedback, usability, technical reliability, student engagement support, accessibility, analytics and reporting, and third-party integration. Quantitative analyses include one-way analysis of variance across all nine dimensions, Bonferroni post-hoc comparisons, Pearson correlation analysis, and multiple regression modelling of the predictors of instructor overall satisfaction. Canvas achieves the highest composite scores for usability, analytics, and integration; Blackboard leads on assessment and reporting depth; Microsoft Teams leads on real-time collaboration; and Zoom leads on content delivery in synchronous sessions but performs poorly on the asynchronous dimensions where dedicated learning management systems are strongest. The paper synthesizes findings into a platform selection framework and eight evidence-based recommendations for practitioners designing or evaluating technology-enhanced teaching environments.

groups

Tariq Saali mail -

Tassawar Kamran mail

link https://doi.org/10.54216/IJAIET.050105

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new