Knowledge Management Systems Development: Theory and Practice
Bibliographic record
Abstract
The intricate crafting of online educational systems lie within three principal activities: Design of the system, implementation, and proper post-implementation assessment.There is not enough knowledge or experience in all regards.Efficient execution of these three major activities necessitates the use of design and pedagogical models to achieve cost and time efficiency, as well as high pedagogical quality.Models represent a structured approach to analysis and promote quantifiable feedback that can be monitored.Components of an online educational system would benefit from a design process.Similarly, utilization of the online educational system would benefit from a structured approach to design, implementation, and student's assessment.Following the technology adoption theory, understanding individual's behavior towards technology usage would focus on instrumental beliefs driving intentions.However, this may not be the case with online educational systems because the context and setup is significantly different from previous technology adoption studies.Therefore, the implementation of an online educational system should be designed based on established pedagogical principles, and once developed the assessment of students' behavior should be monitored using management information systems methodology.In this paper, we present the design of an online education system, and the experience of the students using the system.A survey methodology approach is followed and assessment results are discussed.The technology acceptance model and the theory of planned behavior were used to identify significant constructs as antecedents to intentions.Scale validation for both models indicates that the operational measures have acceptable psychometric properties.Confirmatory factor analysis supports both models.Structural equation analysis provides evidence for the superiority of the theory of planned behavior in explaining students' behavior towards educational online systems.Limitation, implications, design recommendations, and suggestions for future research are then discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".