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Record W3172290871 · doi:10.1139/cjc-2020-0398

Examining chemistry students’ perceptions toward multiple-choice assessment tools that vary in feedback and partial credit

2021· article· en· W3172290871 on OpenAlexaffvenue
Michael Williams, Eileen Wood, Fatma Arslantas, Steve MacNeil

Bibliographic record

VenueCanadian Journal of Chemistry · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPsychologyMultiple choicePreferenceTest anxietyPerceptionAnxietyTest (biology)Mathematics educationStatisticsMathematicsSignificant difference

Abstract

fetched live from OpenAlex

Multiple-choice testing with dichotomous scoring is one of the most common assessment methods utilized in undergraduate education. Determining students’ perceptions toward different types of multiple-choice testing formats is important for effective assessment. The present study compared two alternative multiple-choice testing formats used in a second-year required chemistry course: (i) Immediate Feedback Assessment Technique (IFAT®) and (ii) Personal Point Allocation (PPA). Both testing methods allow for partial credit, but only the IFAT® provides immediate feedback on students’ responses. Both survey and interview data indicated that, overall, most students preferred IFAT® to the PPA testing method. These positive ratings were related to potential increase in reward, ease of use, and confidence. IFAT® was also perceived to be less stress producing and anxiety provoking than PPA. Interview data not only supported these findings, but also indicated individual differences in preference for each of these two methods. Additionally, students’ feedback on strategies used for either testing method and suggestions on how to improve the methods are discussed.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.076
GPT teacher head0.342
Teacher spread0.266 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes2
Has abstractyes

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