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Record W100836835

Inquiry-Based Learning in Experimental Sessions: Strategies towards conducting more effective Experimental Laboratory Sessions with Engineering Undergraduate Students

2011· article· en· W100836835 on OpenAlexaff
Ayodeji Abiola-Ogedengbe

Bibliographic record

VenueScholarship@Western (Western University) · 2011
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsWestern University
Fundersnot available
KeywordsSession (web analytics)Presentation (obstetrics)Mathematics educationTeaching methodComputer sciencePsychologyMedical educationMedicine
DOInot available

Abstract

fetched live from OpenAlex

Experimental sessions in the Laboratory are usually preceded by instructional sessions where student participants are taught about the activities they will be doing in the laboratory. Despite this activity, many times students approach the actual experimentation in the laboratory as mere routine with an expected result. Hence, they are not mentally engaged rather expecting to follow strict procedures as written in the book (manual) and deliver as expected by the book. This is a hindrance to actual learning. This seminar considers an inquiry-based learning approach as a teaching technique for pre-laboratory sessions by Graduate Teaching Assistants which will help engage the undergraduate students more in laboratory activities for productive learning.\nThis presentation is aimed at the Graduate Teaching Assistant (herein referred to as GTA) whose duty usually include preparing students for experimental sessions in the laboratory such as is obtained in MME 2285 (Experimental Methods), a course in Western’s Department of Mechanical and Materials Engineering.\nFor a GTA in this course and similar courses in Engineering, the Professor expects the GTA to hold instructional (taught) sessions with the students ahead of the Laboratory session where he teaches them rudiments of the laboratory experiment and issues pertaining to the laboratory which the students might not grasp in the lecture room typically taught by the Professor. Usually, the lecture room by the professor follow the traditional teaching methods and some students when not able to understand what was taught assume they will understand once they undertake the experiment in the laboratory. However, this is not always the case, so it is important they understand it before actual experimentation commenced. For the GTA through whom students have a second chance at learning from the experimentation, it is usually better to adopt a different teaching method from that which the student earlier encountered in the lecture room. The proposed teaching method is Inquiry-Based learning (herein referred to as IBL) which deviates from the traditional method and engage the student more thereby helping them to understand while complementing what they had been taught as it engages their reasoning. Where students already understood the pre-laboratory sessions in the lecture room with the professor, further teaching through IBL by the GTA will help to engage the student more and help students to mentally adjudge the work they do in the laboratory during the actual experimentation. Ditto, in cases where student erroneously think they understood the first lecture, the IBL session with the GTA can help correct misconceptions and thus avoid/understand potential pitfalls during actual experimentations in the laboratory.\nSince IBL is question driven (Queen University Centre for Teaching and Learning), the GTA will be able to assess the level of understanding of students based on the teachings they have had with the professor. This enables the GTA to understand the specific needs of students as he undertakes the teaching session. The GTA could potentially benefit immensely from this teaching method in his capacity as a Graduate experimentalist as ideas deduced from doing IBL with undergraduate students could be a valuable input in the GTA graduate studies and research.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.329
Teacher spread0.250 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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