The Development of an Instructional Model Based on Flipped Using Technology-Based Learning to Enhance the Digital Literacy for Undergraduate Students in the Faculty of Education, Rajabhat University
Bibliographic record
Abstract
Research objectives were to 1) investigate the current state and best practice of the instruction relying on flipped learning; 2) develop the instructional model based on flipped using technology-based learning to enhance the digital literacy for undergraduate students of Rajabhat University and 3) examine the effects of the model. The sample for fulfilling the 1st objective contained 119 instructors teaching educational technology or related fields within the faculty, 397 students, and 10 experts. Data were collected through 60 first-year students and three types of research tools: the developed model, a test of digital literacy, and a satisfaction questionnaire. Descriptive statistics, that is, arithmetic mean, standard deviation, and t-test were employed in data analysis. The research findings were as follows: 1) The current state of the instruction resulted from the instructors was found to be at an overall lower level (M = 2.43, SD = 0.63) as well as of the students (M = 2.46, SD = 0.65). The participants reflected that the best practice of the instruction included the flipped learning model and learning processes using technologies. 2) The developed model consisted of principle; objectives; learning processes; media, technologies and learning sources; and evaluation. Flipped learning included online and on-site approaches. Learning processes included 6 stages: making knowing learning process; studying from technologies and online learning sources; reflecting and questioning memos; exchanging knowledge; producing tasks; and evaluating. 3) The results of using the developed model showed that 3.1) digital literacy of the students after learning was found to be significantly higher than that of before the study at 0.05; 2) digital literacy of the students in the experiment group was found to be significantly higher than that of the control group at 0.05; and 3) students’ satisfaction was found to be positively highest level (M = 4.81, SD = 0.22).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".