The evaluation and selection of inappropriate open source software in learning management system (OSS-LMS) packages adversely affect the business processes and functions of an organization. Thus, comprehensive insights into the evaluation and selection of OSS-LMS packages are presented in this paper on the basis of three directions. First, available OSS-LMSs are ascertained from published papers. Second, the criteria for evaluating OSS-LMS packages are specified.according to two aspects: the criteria are identified and established, followed by a crossover between them to highlight the gaps between the evaluation criteria for OSS-LMS packages and the selection problems. Third, the abilities of selection methods that appear fit to solve the problems of OSS-LMS packages based on the multi-criteria evaluation and selection problem are discussed to select the best OSS-LMS packages. Results indicate the following: (1) a list of active OSS-LMS packages; (2) the gaps on the evaluation criteria used for LMS and other problems (consisting of main groups with sub-criteria); (3) use of multi-attribute or multi-criteria decision-making (MADM/MCDM) techniques in the framework of the evaluation and selection of the OSS in education as recommended solutions.

Diabetic retinopathy, often resulting from conditions like diabetes and hypertension, is a leading cause of blindness globally. With diabetes affecting millions worldwide and anticipated to rise significantly, early detection becomes paramount. The survey scrutinizes existing literature, revealing a noticeable absence of consideration for computational complexity aspects in deep learning models. Notably, most researchers concentrate on employing deep learning models, and there is a lack of comprehensive surveys on the role of vision transformers in enhancing the efficiency of these models for DR detection. This study stands out by presenting a systematic review, exclusively considering 84 papers published in reputable academic journals to ensure a focus on mature research. The distinctive feature of this Systematic Literature Review (SLR) lies in its thorough investigation of computationally efficient approaches and models for DR detection. It sheds light on the incorporation of vision transformers into deep learning models, highlighting their significant contribution to improving accuracy. Moreover, the research outlines clear objectives related to the identified problem, giving rise to specific research questions. Following an assessment of relevant literature, data is extracted from digital archives. Additionally, in light of the results obtained from this SLR, a taxonomy for the detection of diabetic retinopathy has been presented. The study also highlights key research challenges and proposes potential avenues for further investigation in the field of detecting diabetic retinopathy.

The use of the learning management system (LMS) at the Malaysian Polytechnic is constantly changing according to the current situation. In addition, the relatively low acceptance of LMS in technical and vocational education training (TVET) institutions requires further study. This paper will discuss accurate construct of measurement for LMS TVET using expert consensus through Aiken's V analysis. Based on the analysis coefficient and the reliability of the content, several important constructs have been identified involving system quality, information quality, service quality, motivation, user satisfaction, intention-to-use, self-discipline, practical training, and actual use. Through quantitative analysis, every item in constructs is calculated and reviewed by an expert in order to validate the items. The minimum validity value accepted in this study is 0.75 based on Aiken's V table, thus, two items were rejected. These items were rejected due to the same meaning and being inappropriate. This study proves the instrument's content validity based on expert agreement using the Aiken agreement index. This study contributes to a suitable instrument for measuring LMS in TVET for use in subsequent studies.

Financial sustainability in higher education is increasingly fragile due to policy shifts, rising costs, and funding volatility. Legacy early-warning systems based on static thresholds or rules struggle to adapt to these dynamics and often overlook fairness and interpretability—two essentials in public-sector governance. We propose a university financial risk early-warning framework that couples a causal-attention Transformer with Multi-Objective Bayesian Optimization (MBO). The optimizer searches a constrained Pareto frontier to jointly improve predictive accuracy (AUC↑), fairness (demographic parity gap, DP_Gap↓), and computational efficiency (time↓). A sparse kernel surrogate (SKO) accelerates convergence in high-dimensional tuning; a dual-head output (risk probability and health score) and SHAP-based attribution enhance transparency and regulatory alignment. On multi-year, multi-institution data, the approach surpasses mainstream baselines in AUC, reduces DP_Gap, and yields expert-consistent explanations. Methodologically, the design aligns with LLM-style time-series forecasting by exploiting causal masking and long-range dependencies while providing governance-oriented explainability. The framework delivers earlier, data-driven signals of financial stress, supporting proactive resource allocation, funding restructuring, and long-term planning in higher education finance.

User engagement is a term that reflects both user participation and user involvement. These two aspects of user engagement play an essential role in developing information system successfully. Currently, the lack of theoretical and empirical evidence of factors that influence effective user engagement constitute a need to review relevant literature to determine factors that were directly or indirectly affect user engagement success. A total of 12 candidate success factors have been identified: User-Developer Communication, Identifying Users for Engagement, User Ability in IT Projects, Type of Participation, Top Management Support, Organisational Culture, User Motivation, User Attitude toward System, User-Developer attitude, Conflict Resolution, Involvement Congruence, and Project Complexity. This paper discusses the items for measuring user engagement success. Initially 83 items were identified. These items were first tested by three experts in the area of information system development where two new items were added, and 15 items were deleted after the process. Then each item was validated using content validity ratio and reliability test. Results have indicated that 67 items in the questionnaires are reliable. A valid user engagement success instrument was proposed by the end of this paper.