Student Independent Projects Psychology 2015: Reading, Writing and Mathematics: Computer Assisted Instruction \nas a Learning Intervention (K-9)
Bibliographic record
Abstract
As Computer Technology (CT) has permeated everyday life in Canada, so too has the \nimplementation of computers in classrooms. The 1990’s saw the widespread of the personal PC \nand later the mobile phone. The early 2000’s marked the advent of e-readers and smart phones \nfollowed by the creation of the tablet in 2010. Each form of technology has respectively sparked \na boom in academic research (Li & Ma, 2010). For this paper, I will look at all forms of digital \nscreens under the working definition of Computer Technology (CT) to avoid compounding a \nbroad topic. \nThere is debate about the efficacy of Computer Assisted Instruction (CAI), as research \nindicates similarities and differences in learning through digital and paper mediums. There are \nmany forms of software and computer technology applied to CAI and reading, writing and \nmathematics interventions and educational psychologists and educators have been interested in \nthe efficacy of CT in the classroom to help teach students (Woolfolk et al., 2010). The \nimplementation of CT has been tailored to suit the needs of learners in individual subjects with \ndifferent software designers and different forms of delivery. My purpose was to outline some of \nthe most successful CAI learning intervention methods when compared with paper based \nlearning interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.014 |
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".