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Record W2662252220 · doi:10.18260/1-2--11099

Using Power Point In Distance Learning Laboratories

2020· article· en· W2662252220 on OpenAlexaboutno aff
Richard Cliver

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationPower pointPoint (geometry)Computer scienceQuarter (Canadian coin)VideoconferencingTeleconferenceMaturity (psychological)MultimediaMathematics educationPsychologyMathematics

Abstract

fetched live from OpenAlex

The purpose of this paper is to discuss using power point presentations to enhance circuits laboratories for a distance learning course.Students in this course meet with the instructor for one day instead of two hours for eight weeks during the quarter.Laboratory experiments with directions portrayed with diagrams, photographs, and words were given to the students to do at home before they came to campus with power point presentations.This allowed the students to be more familiar with the material before they traveled to the campus for their full day sessions and alleviated the frustrations they had with earlier experiments performed at home.Background: Distance Learning instruction in engineering education dates back to the late 1960's when Universities began offering graduate courses through this medium.1 Distance Learning offered an easy and affordable way to instruct small classes, the maturity of the Graduate student enabled them to overcome the technical difficulties.2 Distance Learning in undergraduate education is a relativity new development.Rochester Institute of Technology has been participating in distance learning teaching since 1989.RIT has used different formats in their distance-learning classes, which include flexible format, remote classroom, combination and video conferencing.3 In the two years that I have been teaching at RIT, the most popular format has been the flexible format.The distance-learning course to be discussed is Electronic Principles for Design.Students in this class have the course materials delivered to their homes.Lab materials are included with the course material and include a multimeter, resistors, potentiometers, capacitors, power supply and hookup wire.Students in the electronic course I have taught are only required to attend one 8-hour lab session per quarter, where they use lab equipment not available in their kits.They demonstrate proficiency in use of an oscilloscope and function generator through several practical experiments.When I started teaching the electronics course, the lab experiments that were being used were in the old format designed for weekly on campus lab experiments.These labs were well suited for the older format in distance learning where students would come to RIT or find a local community college to attend weekly or 3 -4 times per quarter to complete their lab requirements.In spite of the movement toward the newer flexible format students in this class were merely instructed to do what they could at home and finish the labs during the single all day session on campus.The use of Power Point as shown in this paper allowed me to create new laboratory experiments students could perform at home.These labs contained tutorials that allowed students to acquire important lab skills and complete the labs at home.Parts of the lab tutorials that helped students build their laboratory skills are presented in this paper.The laboratory assignments provided building blocks that will be used in future labs.Page 7.1268.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.009

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 head0.012
GPT teacher head0.223
Teacher spread0.212 · 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 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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