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Record W2594388294 · doi:10.18260/1-2--2045

Student Led Design, Build, Testing And Usage Of In Course Experimental Laboratories

2020· article· en· W2594388294 on OpenAlexaffabout
Khosrow Farahbakhsh, Warren Stiver

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCourseworkComputer scienceComponent (thermodynamics)Process (computing)RecipeSet (abstract data type)CurriculumCourse (navigation)Software engineeringTransfer (computing)MultimediaEngineering managementMathematics educationEngineeringProgramming languageOperating system

Abstract

fetched live from OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Student-Led Design, Build, Testing and Usage of In-course Experimental Laboratories Abstract Laboratory components of engineering courses are traditionally designed and assembled by either course instructors or laboratory technicians. Student’s involvement is most often passive owing to a detailed recipe style set of instructions and frequently recipe style report preparation in which even the relevant axes of figures have been predefined. Mass Transfer Operations (ENGG*3470) is a course that was introduced into the Environmental Engineering curriculum at the University of Guelph in 1998. A lack of facilities initially meant the course started without an appropriate laboratory component. Over the past four years the course has evolved through student designed, built and tested experiments as an integral component of their coursework. Currently, the students are responsible for choosing a mass transfer topic, selecting compounds involved in the mass transfer process, identifying most appropriate analytical techniques, designing, building and trouble-shooting the required apparatus, performing a minimum of two experiments and synthesizing the data in form of a laboratory report. Additionally, the students prepare a laboratory manual that is then used by other students to conduct the particular experiments. Our experience over the past five years indicates that such an approach is not only manageable but also provides the students a unique opportunity to sharpen their design, research as well as communication skills while learning the fundamentals of mass transfer operations. This paper describes the evolution of this approach within the third-year mass transfer course and provides an assessment of its effectiveness on student’s learning. Introduction It is now generally agreed that involving the students in the process of learning and knowledge construction promotes more in-depth understanding, better retention of concepts, increased interest on the subject matter among the students, and stronger problem solving skills. Several approaches have been practiced by educators to ensure meaningful participation of students in learning including problem-based learning1, “learning by doing”2, and “project-oriented education”3 to name a few. All these approaches emphasize a “learner-centered approach” and a move from a “content-based” to a more “context-based” education4. In addition to sharpening student’s laboratory skills, most undergraduate lab-based courses are used to promote some type of hands-on learning. In conventional laboratory course students are provided with detailed instructions on how to perform the work and, in many cases, how to analyze the data. The experimental setup is typically fully laid out by laboratory technologists or graduate teaching assistants and analytical equipment is checked, troubleshoot and calibrated with little or no input from the undergraduate students. In most cases such an approach to undergraduate laboratory experiments is driven by the need to move a large number of students through a lab with limited resources and within a prescribed time period. There are several limitations with the conventional approaches to laboratory exercises in undergraduate courses. Conventional in-course laboratories do not encourage student enquiry

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.247

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 head0.040
GPT teacher head0.314
Teacher spread0.274 · 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 designBench or experimental
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
Published2020
Admission routes2
Has abstractyes

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