MétaCan
Menu
Back to cohort

Audio Feedback: Student and Teaching Assistant Perspectives on an Alternative Mode of Feedback for Written Assignments

2018· article· en· W2893332579 on OpenAlexafffundvenueabout
Fiona Rawle, Mindy Thuna, Ting Zhao, Michael Kaler

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto MississaugaUniversity of Toronto
KeywordsFormative assessmentPeer feedbackAudio feedbackMathematics educationCompetence (human resources)ConstructiveComputer scienceClickerAudio equipmentClass (philosophy)PsychologyPedagogyMultimediaProcess (computing)Engineering

Abstract

fetched live from OpenAlex

Competence in written scientific communication is an important learning outcome of undergraduate science degrees. Writing helps students learn, encourages them to think creatively and critically about their learning, and trains them in communicating their insights as disciplinary experts. However, challenges exist in incorporating writing assignments into large undergraduate science classes, including lack of student engagement and difficulty in providing effective and personalized formative feedback to large numbers of students. Engagement and feedback are especially important for developing writing skills, which require active, reflective, critical attention on the learner’s part: it would be very useful if one mechanism could enhance both. We recently integrated audio feedback into the stages of a term-long, multi-part scientific literacy assignment in a large undergraduate biology class at the University of Toronto Mississauga, using it for formative purposes at early stages of the assignment. In order to determine the utility and effect of the audio feedback, we collected data from both teaching assistants (TAs) and students. In general, students felt audio feedback was constructive and engaging, and both TAs and students commented that audio feedback was more personal than written feedback. However, TAs noted that it took longer for them to give audio feedback compared with written feedback, and that they encountered technical issues with emailing audio feedback to the students. Overall, the response to audio feedback from both students and TAs suggested that this approach is logistically feasible and might aid in overcoming the disengagement that is often found in large introductory courses. La compétence en communication scientifique écrite est un résultat d’apprentissage important dans le cadre des diplômes en sciences au niveau du premier cycle. L’écriture aide les étudiants à apprendre, les encourage à réfléchir avec créativité et sens critique à propos de leur apprentissage et leur donne la formation nécessaire pour communiquer leurs idées en tant qu’experts dans leur discipline. Toutefois, il existe un certain nombre de défis dans le cas de grandes classes de sciences au niveau du premier cycle quand il s’agit d’y incorporer les travaux écrits, entre autres la participation des étudiants et les difficultés à donner à un grand nombre d’étudiants des rétroactions formatives personnalisées. La participation et les rétroactions sont des éléments particulièrement importants pour que les apprenants développent des compétences en écriture, qui exigent de leur part une attention active, réflective et critique. Il serait donc très utile si un mécanisme pouvait inclure ces deux éléments. Nous avons récemment intégré la rétroaction audio dans les diverses étapes de travaux de longue haleine à parties multiples portant sur des connaissances scientifiques dans une grande classe de biologie de premier cycle à l’Université de Toronto Mississauga, avec un objectif formatif dès les premières étapes des travaux. Afin de déterminer l’utilité et les effets de la rétraction audio, nous avons recueilli des données auprès des chargés de cours et des étudiants. En général, les étudiants ont déclaré que la rétroaction audio était constructive et favorisait la participation, et tant les chargés de cours que les étudiants ont indiqué que la rétroaction audio était davantage personnelle que la rétroaction écrite. Toutefois, les chargés de cours ont fait remarquer que cela leur prenait davantage de temps de donner une rétroaction audio qu’une rétroaction écrite et qu’ils avaient eu des problèmes techniques pour envoyer par courriel à leurs étudiants les rétroactions audio. En général, la réaction à la rétroaction audio tant de la part des chargés de cours que des étudiants suggère que cette approche est logistiquement réalisable et pourrait aider à relever le défi d’absence de participation souvent présent dans les cours d’introduction offerts à un très grand nombre d’étudiants.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.037
metaresearch head score (Gemma)0.112
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.112
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.003
Scholarly communication0.0090.004
Open science0.0020.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.070
GPT teacher head0.421
Teacher spread0.351 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations12
Published2018
Admission routes4
Has abstractyes

Explore more

Same venueThe Canadian Journal for the Scholarship of Teaching and LearningSame topicInnovative Teaching MethodsFrench-language works237,207