An Explanatory Sequential Mixed Methods Approach to Exploring Hybrid Learning and Its Influence to Students’ Academic Self-Efficacy, Academic Resiliency and Academic Motivation: Development of an Intervention Plan
Bibliographic record
Abstract
Entering the now normal phase in education, hybrid learning was regarded as the preferred method of instruction in higher education. In order to establish an intervention plan, the study sought to explore how students' hybrid learning influenced the academic self-efficacy, academic resiliency, and academic motivation. The study employed a sequential explanatory mixed-method design. The respondents were Bachelor of Science in Information System and Bachelor of Science in Information Technology college students in selected colleges/universities in second legislative district in Bulacan. The samples were taken using Raosoft calculator, random sampling and purposive sampling. Meanwhile, the data were collected through standardized questionnaires and semi-structured interviews. A descriptive analysis and multiple analysis of variance were used to analyze the quantitative data, while thematic analysis for the qualitative data. According to the findings, this study found that each of the aforementioned aspects has a greater influence on one another when discussing the implementation of hybrid learning, with a focus and emphasis on the level of self-academic skills, academic resilience, and academic motivation. In addition, this study also found that students faced challenges in entering hybrid learning, such as financial issues, technology use, and lack of focus and motivation. Their coping mechanism is resilience in using hybrid learning. In light of the study's findings, the researcher insisted that it is necessary for administrators, teachers, staff, and students to embrace this advancement and that the school's management of pedagogical skills, which enhance students' autonomy, competence, perseverance, and self-regulation, should be strengthened. The researcher highly recommends implementing the proposed intervention plan to promote the well-being of students amidst the continuous advancement in academia. The institutionalization of an intervention program will alleviate the struggles and challenges of students in their academic activities.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".