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Record W2092367835 · doi:10.1109/te.2011.2160946

A Control Systems Concept Inventory Test Design and Assessment

2011· article· en· W2092367835 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

VenueIEEE Transactions on Education · 2011
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsConcept inventoryTest (biology)Consistency (knowledge bases)Computer scienceClassical test theoryItem response theoryTest designTest theoryControl (management)Internal consistencyMultiple choiceTest scoreMathematics educationTest methodArtificial intelligencePsychologyPsychometricsMathematicsStandardized testStatistics

Abstract

fetched live from OpenAlex

Any meaningful initiative to improve the teaching and learning in introductory control systems courses needs a clear test of student conceptual understanding to determine the effectiveness of proposed methods and activities. The authors propose a control systems concept inventory. Development of the inventory was collaborative and iterative. The diagnostic test was administered to students before (pre) and after (post) student learning activities. Test responses were analyzed to provide instructors with feedback on their teaching. Using classical test theory and item response theory, aggregated results were analyzed to assess internal consistency and measurement error, respectively. Students demonstrated an improvement from pre- to post-test scores, showing gains of 23%-34% in understanding of new concepts learned. The internal consistency of the test has ranged from 0.61 to 0.68. It can be shown that the precision of the test is highest in the score range of 33%-66%, which is where most post-test scores occurred.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.535

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 head0.019
GPT teacher head0.245
Teacher spread0.226 · 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