Investigating the Impact of Model Eliciting Activities on Development of Critical Thinking
Bibliographic record
Abstract
Abstract Investigating the Impact of Model Eliciting Activities on Development of Critical ThinkingModel eliciting activities (MEAs) are realistic problems used in the classroom that requirelearners to document not only their solution to the problems, but also their processes for solvingthem. MEAs have been developed and used in a variety of subject areas, including mathematics,economics, and environmental engineering. Studies have shown MEAs to be valuable in helpingstudents to develop conceptual understanding, knowledge transfer, and generalizable problem-solving skills.MEAs have been integrated into a first-year undergraduate engineering course at a medium-sizedCanadian university. Students in this course are asked to work collaboratively on three differentMEAs, each introduced in a three- to four-week cycle. While each MEA requires students toemploy different areas of subject knowledge, students are taught to approach all three MEAsusing critical thinking skills. For example, students are guided to draw concept maps, questionthe credibility of information sources, incorporate a range of factors into their decision-making,and consider the implications of their conclusions. These skills are what Paul (2006) calls“elements” of critical thinking—invaluable thinking processes involved in any complexproblem-solving activity.A research team has been formed at the university to investigate the impact of the MEA-integrated course on students’ development of critical thinking skills. Ultimately, the team aimsto determine whether the MEA-integrated course facilitates students’ critical thinking. Presently,the team has developed two mini-MEAs to be used as pretest and posttest instruments. They aresimilar to the three MEAs introduced in the course, in that they are set in realistic contexts, butthey are simpler, in that students do not need to do any modeling in labs and are given readingmaterial that is shortened and directly relevant to issues embedded in these MEAs. The paperpresented discusses a pilot study the team conducted on one of the mini-MEAs, which we callmini-MEA A.The purpose of this pilot study was twofold: first, to explore thinking processes involved insolving mini-MEA A; and second, to develop a standard procedure for identifying and evaluatingthinking processes involved. The method employed for eliciting students’ thinking processes wascalled the think-aloud, in which involve participants think aloud while solving a problem. Afterthis the researcher analyzes their verbal and written products, which are known as “think aloudprotocols.” Three upper-year engineering students who had no exposure to the MEA-integratedcourse were randomly selected for the study. Prior to signing consent forms, all three participantswere briefed about the purpose and procedure of the study. The entire think-aloud session lastedfor about one hour, was video-taped, and transcribed and annotated.The research team divided students’ think aloud protocols into five segments. Each segmentconsisted of a particular issue with which the group tackled. Drawing on Paul’s theoreticalframework for critical thinking, the research team found that while students did display criticalthinking in each segment, the quality of this thinking could be greatly improved. For example,the group was able to make reasonable safety recommendations, but the students made severalrecommendations without critically examining their own assumptions or those of the informationsources provided to them. In this presentation, the research team will show several examplesdrawn from the think aloud protocols and discuss how Paul’s theoretical framework can be usedto evaluate students’ thinking processes. In addition, the research team will discuss theadvantages and disadvantages of using mini-MEAs as pretest and posttest tools for investigatingthe impact of MEAs on students’ critical thinking skills.
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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 | 0.024 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".