Technology as a resource: Increasing engagement in learning and developing 21st century skills
Bibliographic record
Abstract
The purpose of this research study was to help inform the researcher’s future practice as a teacher on the influence of technology on young learners. The question of inquiry pertained to define how technology has initiated and increased student engagement within their learning, and through this, how technology has specifically influenced the creation of the 21st century learner. By beginning the inquiry through a literature review on the theme of technology and student engagement, the researcher was able to gain an understanding that technology is the motivator for children in developed, and even in undeveloped countries. In order to assess the relationship of technology and student engagement in learning, along with how digital devices influence the creation of 21st century learners, the researcher surveyed a variety of teacher candidates and university students to draw conclusions. From the research findings of the study, the researcher concluded that students were attracted to technology as it is a tool that is convenient in organizing information, and is efficient in making data easier to allocate. Therefore, the skills that technology imparted upon 21st century learners from the findings were allowing students to become self-directed learners, access and, evaluate information through critical thinking and solving data to become more engaged thinkers, and influencing leadership and collaboration skills.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".