Instructional Technology adoption at Higher Educational Institutions using Post-PC Technology
Bibliographic record
Abstract
“Technology is a driving force in education, opening up many doors and preparing students for what lies ahead, not behind,” said Kirsty Kelly, Primary Years Program Coordinator at the Canadian International School in Singapore. Educational systems in the 21st century are affected by the massive accelerations in technology development. The practice of learning is now outside the classroom more than inside. The student in this system seeks fruitful learning where the skills are needed as the scientific value. Instructional Technology (IT) is an integrated system that prepares and evaluates the educational process to achieve the desired learning goals; this can be achieved by understanding local practice and using IT insights to design more attuned interventions (Hora & Holden (2013)). The proposed research aims to assess the impact of offering a range of affordances to instructors and students on the teaching and learning processes. The proposed pilot educational research is undertaken on a focus group with two instructors and approximately 40 IT students at UTAS-Suhar; to explore the use of Post-PC technologies for applying an interactive and customised curriculum and to add enhancement to the learning process and outcome. Using the latest training/educational technologies enhances the effectiveness of learning environments. Therefore, this research studied the impact and outcomes of Instructional Technology (IT) adoption at Higher Educational Institutions (HEIs) using Post-PC Technologies. The research was planned to conduct an experiment on four groups of Information Technology majors, where two groups will apply IT, and the other two will use the traditional learning strategy. First, a pilot online survey was conducted with the two IT groups to collect data about their interest and expectations about the IT techniques. The survey results were used to examine how students’ performance, success, and achievements are affected by using Post-PC Technology as per their responses. Tableau data visualisation tool was used to analyse and visualise the collected data and compare the students’ performance in 4 groups. This helps to emphasise the importance and impact of IT on students’ learning process and achievements at HEIs.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".