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Record W3188631869 · doi:10.18260/1-2--37489

Meaning to Succeed: Learning Strategies of First-Year Engineering Transfer Students

2024· article· en· W3188631869 on OpenAlex
Natalie Van Tyne, Lisa McNair, David Reeping

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2021 ASEE Virtual Annual Conference Content Access Proceedings · 2024
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsImpact
FundersUniversity of MichiganNational Science Foundation
KeywordsCourseworkEngineering educationAttritionMeaning (existential)Mathematics educationComputer scienceInstitutionTransfer of learningEconomic shortageEngineering managementEngineeringArtificial intelligencePsychologySociologyMedicine

Abstract

fetched live from OpenAlex

Meaning to Succeed: Learning Strategies of First-Year Engineering Transfer Students This Evidence-Based Practice paper will describe the learning strategies used by first-year engineering transfer students in an introductory engineering design course. First-year engineering students, including transfer students, often develop the habit of working on homework assignments and studying for tests at the last minute, “just-in-time” to meet a deadline. While “just-in-time” processes may work well in manufacturing or when handling fresh foods, the habit of attempting to learn at the last minute often results in poor quality work and poor performance on tests, causing students to wonder what they actually learned when they reflect on their course experiences later on.\n\nBy neglecting to set priorities, monitor progress, and search for the underlying meaning of their course material, these students will do well enough to “get by” or even achieve success during their first year courses, and yet falter when they encounter a greater need for conceptual knowledge coupled with a heavier course workload in their upper level courses. Students who approach their studies with the intent to “get by” without engaging with course material in a meaningful way are said to have a “surface” approach to learning, as opposed to a “deep” approach that demonstrates an engagement with learning. A third approach has also been identified, called a “strategic approach”, where the intent is to achieve high grades, whether through a “surface” or “deep” approach, or a combination of the two.\n\nMany of our transfer students who are taking a required and customized one-semester first-year engineering course are also taking more advanced foundation courses, in which they are already experiencing the greater academic intensity of the sophomore year of an engineering curriculum. As a result, they may choose to disengage from their course work rather than apply an approach to learning that would enable them to be more successful. In the extreme case, some may leave an engineering program to switch to one with less rigor. Since greater interaction with faculty has been shown to encourage greater academic engagement, guided practice in meaningful learning approaches and strategies should be provided to students, especially those who persist in following the “surface” approach.\n\nA better understanding of the existing learning approaches and strategies used by these students is a necessary starting point for the mitigation of possible academic disengagement. Therefore, this study will focus on the learning approaches and strategies used by first-year engineering transfer students, in order to inform educators about what types of guided practice may be useful to encourage these students to adopt and/or reinforce learning strategies that will help them to be more successful in their concurrent and future courses. Existing literature has provided little evidence about the learning approaches and strategies of traditional-aged first-year engineering students, much less engineering transfer students.\n\nData will be collected through written essays by approximately 100 participants as a homework assignment. The assignment contains prompts about how the participants plan their study schedule, identify their most useful methods for studying, decide when they have “studied enough” for a test or worked “long enough” on a project, participate in a study group, and indicate how they regard the certainty of their study materials. The data will be analyzed by coding for specific types of learning strategies associated with a “deep” vs. “surface” approach to learning, with codes for both cognitive and metacognitive learning strategies such as setting goals, self-questioning and self-testing, peer learning, and using reflection to summarize or draw conclusions. The essay prompts are not intended to be course-specific, and the participants will be encouraged to differentiate their learning strategies among their courses.\n\nVariations are anticipated in the use of learning strategies among courses. In addition, course delivery methods, as described by participants, will exert an effect on a participant’s choice of learning strategies, because they convey instructors’ expectations for both mastery and performance. Results will be compared to those for a cohort of traditional-aged first-year engineering students for similarities and differences in approaches to learning and choices of learning strategies. This study is intended to discern the extent to which engineering transfer students are more, or less, academically engaged than their entering first-year counterparts.

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.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.023
GPT teacher head0.262
Teacher spread0.238 · 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