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Using Educational Escape Room Activities to Teach Teamwork Skills and Build Effective Teams

2019· article· en· W3173798267 on OpenAlexaff
John Kelly, Nicole Campbell

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsWestern University
Fundersnot available
KeywordsTeamworkSession (web analytics)Set (abstract data type)Medical educationThematic analysisPsychologyWork (physics)Mathematics educationComputer scienceEngineeringMedicineQualitative research

Abstract

fetched live from OpenAlex

Teamwork involves a set of skills used by a group of people who are working together towards a common goal. It is one of the most important skill sets for many careers; however, undergraduate students report that opportunities to develop teamwork skills are limited. Additionally, knowing the criteria that should be used to build effective teams of students can be challenging for instructors. In response to these problems, our aim was to develop and evaluate a low‐stakes team‐based activity that provides students with an opportunity to practice and develop teamwork skills. We also used the activity to investigate the criteria required to select effective student teams. To do this, we collaborated with a local escape room design team to develop a novel educational escape room activity (escape activity) that could be implemented in upper year science laboratory courses. Briefly, the activity requires students to work in teams and consists of various thematic challenges, such as visual and numerical puzzles. It requires collaboration amongst team members, each with their own strengths and perspectives, to solve a series of challenges and “break‐in” to the box within a limited amount of time. Currently, a longitudinal study approved by our institutional review board is being completed in a fourth‐year undergraduate laboratory course, which requires students to work in teams throughout the semester on a research project. The escape activity was run during the first lab session and students were randomly placed in small groups. Following the activity, students completed a validated self‐efficacy of teamwork skills survey and reported on their past research experiences. The results of the surveys allowed us to build teams that had strengths in teamwork skills, research experience, or both. When building teams, we ensured that they all represented a diversity of skills so that no teams were disadvantaged. An additional survey on self and peer evaluations of teamwork behaviours will also be administered at the end of the semester to evaluate the effectiveness of these teams. This will be done using a t‐distribution test on the teams' scores. The results of these surveys could inform the criteria that are used to select effective student teams in the future. Individual and group‐based reflections completed after the activity and at the end of the semester will be analyzed to identify themes related to teamwork concepts that students learned and compared to surveys on past teamwork experiences. These will assess the students' development of teamwork skills and their perceptions of its importance throughout the semester, which could inform the effectiveness of the escape activity as a pedagogical tool to teach teamwork skills. The data is in the process of being collected and analyzed. The escape activity is generalizable to any course or discipline. Thereby, validation of the escape activity as an effective pedagogical tool to teach teamwork skills could provide educators with an innovative opportunity to address the lack of opportunities for teamwork development in the undergraduate curriculum. It could also enhance students' self‐efficacy of teamwork skills which could provide educators with criteria to develop more effective student teams. Support or Funding Information Western CTL This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.321

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 head0.004
GPT teacher head0.223
Teacher spread0.220 · 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
Published2019
Admission routes1
Has abstractyes

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