Usage of Learning Management System (Moodle) among Postgraduate Students: UTAUT Model
Bibliographic record
Abstract
The application of a learning management system (LMS) Moodle is learning and teaching platform in Universiti Utara Malaysia. To examine the level of acceptance of this technology, the UTAUT (Unified theory of acceptance and use of technology) Model is used to infer individual students’ technology acceptance by explaining the variants in Behavior Intention (BI). This study is conducted on 65 postgraduate students pursuing their study at UUM. The students are all studying the same course and they are exposed to the application of LMS known as ‘Moodle UUM Learning Zone’. A set of questionnaire, in the UTAUT Model which is developed by Venkatesh et al. (2003), is used to collect data which is then descriptively analyzed by using IBM SPSS Statistics Version 20 and SmartPLS 2.0. The findings of the study found that Performance Expectancy (PE) (?=0.418, p<0.01), Social Influence (SI) (?=0.238, p<0.01) and Facilitating Conditions (FC) (?=0.120, p<0.01) have positive influence towards ‘Behavioral Intention’ (BI). The value R2 = 0.520 showed that 52.0% of the variants in the application of Learning zone can be explained by Behavioral Intention (BI). Consequently, the result related to moderator influence in terms of gender showed that all the four UTAUT Model constructs failed to reject HO5. The results also showed that moderator influence in terms of gender with PE, EE, SI and FC does not have significant positive influence towards BI. The findings of this study which are hoped to help encourage instructors and students to use this technology in their learning and teaching processes, have proven that LMS ‘Moodle’ is beneficial and effective for learning and teaching processes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".