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Teaching Undergraduate Students to Create and Use Concept Maps to Improve Their Learning

2020· article· en· W3016510253 on OpenAlexaff
Anita Woods, Angela Beye

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsWestern University
Fundersnot available
KeywordsLikert scaleMathematics educationClass (philosophy)Construct (python library)Concept mapScale (ratio)Problem statementPoint (geometry)Active learning (machine learning)PsychologyTeaching methodComputer scienceMathematicsArtificial intelligenceManagement scienceCartography

Abstract

fetched live from OpenAlex

The use of concept maps have been shown to be a more effective study method, however students report that they rarely adopt this practice and resort to highlighting and rereading their notes. It is possible that students are intimidated to attempt new, unfamiliar strategies and that with some training, they may be more likely to employ better methods. The purpose of this study was to assess if we could modify student study behaviours through modelling various active learning strategies throughout the year, including concept mapping. As part of this study, students in our first and second year undergraduate human physiology courses were invited to participate. These courses had mandatory tutorials each week where students worked in small groups that we had assigned. During the year, different types of active learning methods were introduced. Among these, two tutorials were designed to model the use of concept mapping. In the first semester, students were given a partially constructed concept map on skeletal muscle physiology to demonstrate how concept maps can be used to connect material they learned in class. In the second semester, the students were given a list of terms and concepts related to renal physiology and were asked to construct their own maps. In our first tutorial, we surveyed students to determine the learning strategies they currently employed using a modified strategies for learning questionnaire (pre‐MSLQ). Participants used a 7 point Likert scale to answer the 30 item survey, with a value of 1 indicating a strong disagreement to the statement and 7 reported for strongly agreeing. Again at the end of the year, students were asked to complete the same modified strategies for learning questionnaire (post‐MSLQ). We compared this to the pre‐MSLQ to determine if their methods used to approach studying course material had changed with regards to the learning activities, including concept mapping. Additionally, at the end of the year, students were asked to complete a learning attitudes survey, which evaluated the students’ perceptions of learning achieved with the use of these learning techniques modelled in their tutorial times. When comparing the pre and post‐MSLQ surveys, participants reported a score of 4.6 in the pre‐course survey, but an increased score of 5.4 in the post‐course survey (p<0.01, paired t‐test) to the statement “When I am studying a topic, I try to make everything fit together into a “big picture” (ie. charts, concept maps). In the learning attitudes survey, preliminary data from the statement “I feel I learned more when I used concept maps in the learning process than when I used my usual study techniques”, 60% of students reported that they agreed with this statement. Meanwhile, 70% of participants agreed with the statement, “I hope to employ concept maps in future learning activities”, suggesting that they found the activity beneficial. In follow up studies, we hope to determine the quality of created concept maps and analyze if this correlated to differences in participants performances on evaluations in the course. Since participants also reported an increase in the use of other active learning techniques, such as peer teaching, we also plan to explore whether these adopted techniques may contribute to any observed improvements in course evaluations.

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)0.004
metaresearch head score (Gemma)0.011
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: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.003

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 head0.060
GPT teacher head0.375
Teacher spread0.314 · 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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Citations0
Published2020
Admission routes1
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