Examination of students’ acceptance and intention to use a New LMS during COVID-19 pandemic
Bibliographic record
Abstract
The aim of this research is to study the acceptance of university students to use Microsoft Teams e-Learning system and their intention to use it as a Learning Management System (LMS) for education during the COVID-19 pandemic in Jordan. An ex-tended Technology Acceptance Model (TAM) with a blend of external factors that are used together for the first time was developed and used for the purpose of this study. TAM was used because of its wide use and success during the past few years for evaluating the influence of different factors affecting the acceptance and intention to use e-Learning platforms within educational institutes. However, all the studies were examining the variables and factors affecting the behavioral intention and acceptance to use LMSs when normal and conventional classroom study is available. In this research, seven external variables, in addition to the four TAM variables, were introduced in a model including one external variable, Internet Connectivity (IC), used for the first time in the field of education. A model is constructed by extending TAM with the introduced external variables, hypotheses are constructed and a questionnaire for 396 students at two universities in Jordan is conducted. Reliability, confirmatory factor, model fit, and hypothesized structural model analyses are presented. Results show that all the variables tested affect, either directly or indirectly, the acceptance and intention to use MS Teams during the pandemic. 21 hypotheses were tested between the constructs and found significant except the relations between (Social Norm - Perceived Usefulness) and (Technical Support - Perceived Usefulness).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".