Using a Modified Technology Acceptance Model for a Learning Management System Platform: A Questionnaire Design for Evaluating the Blackboard Learning Management System
Bibliographic record
Abstract
Educational approaches have advanced and changed tremendously in recent years. The evolution of communication technologies has been important, particularly since many educational activities have been moved to an e-learning format. This has introduced innovative teaching methods and educational management techniques. Learning management systems (LMS) such as Web-based course development tools (WebCT), Blackboard, and learning spaces are currently widely accessible. Most higher education institutions use Blackboard as their primary LMS platform, making it one of the most frequently used LMS platforms. Blackboard technology is a widely used online program for educational institutions that makes it easier to distribute crucial items from teachers to students, such as papers, student reports, projects, and other publications. Consequently, the main objective of this paper is to design a technology acceptance model to investigate user acceptance of a Blackboard learning management system. In addition, we aim to create a quantitative strategy using a technology acceptance model (TAM) questionnaire as the major investigation tool. The Blackboard learning management system is evaluated using a quantitative approach based on the TAM. We use perceived usefulness, perceived ease of use, user satisfaction and attributes of usability as the associated constructs for evaluation. All of these structures were altered to fit the study's needs. Furthermore, in this paper, we examin the specifics of each concept, and how they relate to the research problem. The study's findings indicate a set of methodologies that can be used to evaluate the benefits of using the Blackboard system for online learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".