Key Information-Problem Solving Skills to Learn in Secondary Education: A Qualitative, Multi-Case Study
Bibliographic record
Abstract
Internet has become one of the most important information sources for students’ personal and academic life. In addition, the World Wide Web is receiving increased attention in education because of its potential to support new forms of learning. However, using the information from the net for learning requires the development of a set of abilities such as searching and tackling information from the Internet to find solutions of a problem—this set of abilities is called Information-Problem Solving (IPS) skills. The main objectives of this study are the following three: first, to provide a detailed description about how secondary students solve an IPS task; second, to identify key IPS skills, sub-skills, and regulation activities that have more incidence upon students’ success to solve a problem using digital information on the Web; and third, to use this information to draw educative guidelines to design web-based instructional process and foster IPS in secondary classrooms. In-depth analyses of quantitative and qualitative data of a multi-case study allowed us to identify distinctive patterns and sequences of IPS skills used by students to solve a task. Furthermore, IPS skills (defining the problem and search for information), sub-skills (specifying search terms and selecting results from a SERP), and regulation activities (orientation on the task, monitoring, and testing) were identified as key skills which have more incidence in students to solve successfully IPS tasks to learn curricular contents at school.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".