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Record W2474708249 · doi:10.1145/2899415.2899457

Factors for Success in Online CS1

2016· article· en· W2474708249 on OpenAlexafffund
Jennifer Campbell, Diane Horton, Michelle Craig

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsSection (typography)Drop outOnline learningPsychologySuccess factorsFace-to-faceComputer scienceTest anxietyAnxietyMedical educationMathematics educationMultimediaMedicine

Abstract

fetched live from OpenAlex

Enrollment in post-secondary online courses has been increasing, but several studies have found that the drop rates in online courses are higher than in face-to-face. In our previous study comparing an online section of CS1 with a face-to-face flipped section, we also found the drop rate higher in the online section. Given that we plan to continue offering online options for our students, we aim to identify factors associated with success in online CS1. In this paper, we examine factors that are under students' own control such as how fully they participate in ungraded but important learning activities, and other factors that we may be able to manipulate and improve, such as students' skills for self-regulated learning, and their sense of community in the course. We found important differences between the online and flipped sections regarding what behaviours and attributes were associated with success. While completion of unmarked practice exercises was a factor for both sections, test anxiety and self-efficacy were factors only for the online section, and intrinsic goal orientation was a factor only for the flipped section.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score0.991

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 head0.058
GPT teacher head0.380
Teacher spread0.322 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations40
Published2016
Admission routes2
Has abstractyes

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