Achieving and Underachieving Students' Problem-Solving Performance: Detection of Linguistic Ambiguity, Reflection-Impulsivity, Tolerance-Intolerance of Ambiguity, and Hypothesis Testing
Bibliographic record
Abstract
ABSTRACT \nThis study is an investigation of problem solving in achieving and underachieving fourth and eighth grade students. Using a regression-referenced procedure, 30 students at each grade level were identified as achievers or underachievers on the basis of their scores on the Canadian Cognitive Abilities Test and the Canadian Test of Basic Skills. A multidimensional approach to problem solving resulted in the assessment of students' ability to deal with problems through the detection of (a) linguistic ambiguity in problems, (b) perceptual ambiguity as measured by the Matching Familiar Figures-20 test, (c) perceptual ambiguity in problem situations by the "How I Feel About Problems" scale, and by (d) hypothesis testing in discrimination learning problems. \nThe data for the four experiments were initially analyzed separately. Results of the experiments on linguistic ambiguity and hypothesis testing revealed that achieving students are better able to deal with linguistic ambiguity in riddles are are better focusers as evidenced by their more efficient use of feedback and a greater percentage of logically correct hypotheses than are underachieving students. Eighth grade students are better problem solvers than fourth grade students as indicated by (a) superior detection of ambiguity in riddles, (b) efficiency as measured by the MFF-20 test, (c) greater tolerance of ambiguity, and (d) better focusing as revealed by their proportion of logically correct hypotheses. \nIntercorrelations among linguistic ambiguity, ambiguity tolerance, reflection-impulsivity, efficiency-inefficiency, hypotheses logically correct, and hypotheses latency resulted in several significant correlations in the direction expected. For achievers, linguistic ambiguity was negatively correlated with ambiguity tolerance and with hypotheses logically correct; ambiguity tolerance was negatively correlated with hypothesis latency. For underachievers, linguistic ambiguity was negatively correlated with ambiguity tolerance and positively correlated with reflection-impulsivity and efficiency; reflection impulsivity was positively correlated with efficiency and negatively correlated with hypotheses logically correct and hypothesis latency. \nDiscriminant analysis permitted examination of the combined data on the four variables for the fourth, eighth, and fourth and eighth grade students combined. For the fourth grade students, linguistic ambiguity and hypotheses logically correct accounted for 13% of the discriminatory power of the discriminant function which differentiated achievers from underachievers. Sixty-five of the students were classified correctly. For the eighth grade students, linguistic ambiguity, reflection-impulsivity, and hypotheses logically correct accounted for 19% of the discriminatory power of the discriminant function which differentiated achievers from underachievers. Sixty-seven percent of the students were correctly classified. For the fourth and eighth grades combined, hypotheses logically correct, linguistic ambiguity, and the ambiguity tolerance accounted for 11% of the discriminatory power of the discriminant function which differentiated achievers from underachievers. Seventy-one percent of students were correctly identified. \nFindings from the present study indicate that problem solving is related to academic achievement. They also suggest that achieving students are using different strategies as indicated by their superior focusing on discrimination learning problems. The anticipated developmental improvement in performance from the fourth to eighth grade was also found.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".