Bibliographic record
Abstract
<p id="C2">Problem-solving competence is defined as the capacity to engage in cognitive processing to understand and resolve problem scenarios where a solution is not obvious. Computer-based assessments usually provide an interactive environment in which students can solve a problem by choosing among a set of available actions and taking one or more steps to complete a task. All students’ actions are automatically recorded in system logs as coded and time-stamped strings. These strings are called process data. The process data have multi-level structures in which the actions are nested within a single individual and therefore they are logically interconnected. Recently, researches have focused on characterizing process data and analyzing the response strategies to solve the problem. <p id="C201">This study proposed an extended MMixIRT model which incorporated the multilevel structure into a mixture IRT model. It can classify latent groups at process level that have different problem solving strategies, and estimate the students’ abilities at the student level simultaneously. This model takes the accumulated response information as the specific steps at the process level and defines a more free matrix to determine the weight information used for ability estimation at the student level. Specifically, in the standard MMixIRT model, the student-level latent variables are generally obtained from the measurement results made by the process-level response variables, while students’ final responses are used to estimate their problem-solving abilities in the extended MMixIRT model. <p id="C202">This research applied process data recorded in one of the items (Traffic CP007Q02) of problem solving in PISA 2012. The samples were 3196 students from Canada, Hongkong-China, Shanghai-China, Singapore, and America. Based on the log file of the process record, there were 139,990 records in the final data file. It was found that (1) The model can capture different problem-solving strategies used by students at the process level, as well as provide ability estimates at the student level. (2) The model can also analyze the typical characteristics of students’ strategy in problem-solving across different countries for targeted instructional interventions. <p id="C203">It is concluded that the extended MMixIRT model can analyze response data at process and student levels. These analyses not only play an important role in the scoring, but also provide valuable information to psychometricians and test developers, help them to better understand what distinguishes well performing students from the ones that are not, and eventually lead to better test design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.030 |
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; both teacher heads agree on what is shown here.
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".