Assessment of a Survey Instrument for Measuring Affective Pathways
Bibliographic record
Abstract
This research paper analyzes the emotions that students experience while completing ill-defined complex problems called Open-Ended Modeling Problems in their engineering courses.Students are asked to make their own modeling decisions, rather than being given those assumptions, as is the case in most textbook problems.There are many approaches they can take, and having to make decisions and assumptions that impact the problem has been found to generate strong emotions.Goldin's research on mathematics education asserts that students tend toward affective pathways while completing problems.An affective pathway is the sequence of emotions that a student goes through while solving a problem.Goldin theorizes that there are two main categories of affective pathways that students fall into: positive pathways and negative pathways.This paper builds on our previous work on the development of a survey instrument to quantitatively measure affective pathways.The survey asked students to drag and drop emotions into the order they experienced them during their problem solving process.In this study, we sought to improve upon our survey instrument.Based on our previous research, we added several emotions and alphabetized the list to see whether the order of words impacted the responses.Here, we examine the results from an updated survey question as well as a small set of interviews conducted to investigate how students approach answering the survey question by having them think aloud while completing it.The survey was sent to six classes at five universities, and interviews were conducted with six students at two of those universities.Through our analysis, we found that most students feel confused or frustrated at some stage, and that their emotions change as they continue from start to finish, which is in line with the findings of the previous version of the survey instrument.We are looking further into whether the students turned their frustrations into the positive or negative pathways that Goldin describes.From the interviews, we found most of the verbalized pathways matched what was submitted through the survey instrument.However, there were instances where the submitted and verbalized pathway did not match, suggesting further changes to the question's implementation.Developing a reliable method for measuring affective pathways will enable future study of why and when positive or negative pathways occur, as well as potential actions that engineering educators can take to help students interrupt negative pathways.Goldin's work suggests that negative pathways influence students' global affect, which could impact retention in engineering.
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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 | 0.053 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".