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Record W3133543312 · doi:10.1145/3408877.3432467

Using a Computer to Score Parsons Problems Answered on Paper

2021· article· en· W3133543312 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCode (set theory)Computer scienceENCODEBubbleSoftwareMathematics educationProgramming languagePsychology

Abstract

fetched live from OpenAlex

Parsons Problems are code rearrangement problems that can be used to assess students' programming ability. Our work builds on previous studies involving Parsons Problems by showing that it was possible to have students encode the solution to a Parsons Problem on a special purpose bubble sheet. This allowed students to answer Parsons Problems in a traditional paper-based exam environment while also allowing their responses to be scanned and scored automatically. The nature of the special purpose bubble sheet is described, as is the proof-of-concept software that was developed to analyze the bubble sheets, evaluate the student responses, and distribute the graded responses back to the students. Written feedback received from students on end-of-course surveys showed that the vast majority of students considered electronically scorable Parsons Problems to be a fair and reasonable evaluation technique in a large introductory course, and only minimal concerns were raised about the need to record their solutions on a bubble sheet.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.915
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.295
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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