Incentives and Obstacles Influencing Faculty Members’ Use of Information and Communication Technology (ICT)
Bibliographic record
Abstract
The use of Information and Communication Technology (ICT) in the classroom is very important for providing opportunities for students and learners to learn to operate in an information era. Studying the incentives and obstacles to the use of ICT in education may assist instructors to overcome these barriers and become successful technology adopters in the future. Thus, this paper investigates the factors related to the incentives and obstacles that influence the use of ICT in the educational practices as perceived by faculty members in Jordanian universities. Moreover, this article identifies factors that may affect faculty member’ decisions to use ICT in the classroom. These factors are interrelated; the success of the implementation of ICT in teaching and learning process is not dependent on the availability or absence of one individual factor, but is determined through a dynamic process involving a set of interrelated factors. A web-based questionnaire was employed and distributed to all faculty members of four selected universities in Jordan for the Fall Semester 2017/2018. A total of 262 participants from a wide variety of schools have responded and completed the survey. The results of the analysis revealed that the major factors that prevent or affect faculty member’ use of ICT in higher education institutions in Jordan are lack of faculty member ICT skills; lack of faculty member confidence; lack of pedagogical faculty member training; lack of suitable educational software; limited access to ICT tools; rigid structure of traditional education systems; restrictive curricula, etc. The article concluded that knowing the extent to which these factors and obstacles affect individuals and institution may help in taking a decision on how to tackle them.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".