Opinions on grading and difficulties encountered by physical education and health teachers in relation to assessment
Bibliographic record
Abstract
Background: Assessment plays a key role in quality physical education and health [PEH; Leirhaug and Annerstedt (2016. “Assessing with New Eyes? Assessment for Learning in Norwegian Physical Education.” Physical Education and Sport Pedagogy 21 (6): 616–631. https://doi.org/10.1080/17408989.2015.1095871)]. López-Pastor et al. (2013. “Alternative Assessment in Physical Education: A Review of International Literature.” Sport Education and Society 18 (1): 57–76. https://doi.org/10.1080/13573322.2012.713860) identify assessment as ‘one of the most fraught and troublesome issues physical educators have had to deal with over the past 40 years or so’ (57). Three main issues emerged from studies conducted in the last 15 years on PEH teachers’ assessment practices: (1) a lack of consistency between what is taught and what is assessed [Borghouts et al. (2016. “Assessment Quality and Practices in Secondary PE in the Netherlands.” Physical Education and Sport Pedagogy 22 (5): 473–489. https://doi.org/10.1080/17408989.2016.1241226); Svennberg et al. (2018. “Swedish PE Teachers Struggle with Assessment in a Criterion-Referenced Grading System.” Sport, Education and Society 23 (4): 381–393. https://doi.org/10.1080/13573322.2016.1200025); Williams et al. (2020. “‘I Think Everyone Is on Board with Changing How We Do Things, But We Are Yet to Find a Best Fit Model’: A Figurational Study of Assessing Games and Sport in Physical Education.” Sport, Education and Society 26 (3): 253–266. https://doi.org/10.1080/13573322.2020.1716212)); (2) a lack of knowledge among PEH teachers regarding assessment[(Borghouts et al. (2016. “Assessment Quality and Practices in Secondary PE in the Netherlands.” Physical Education and Sport Pedagogy 22 (5): 473–489. https://doi.org/10.1080/17408989.2016.1241226); Williams et al. (2020. “‘I Think Everyone Is on Board with Changing How We Do Things, But We Are Yet to Find a Best Fit Model’: A Figurational Study of Assessing Games and Sport in Physical Education.” Sport, Education and Society 26 (3): 253–266. https://doi.org/10.1080/13573322.2020.1716212)] and (3) the often intuitive and subjective nature of assessment [Borghouts et al. (2016. “Assessment Quality and Practices in Secondary PE in the Netherlands.” Physical Education and Sport Pedagogy 22 (5): 473–489. https://doi.org/10.1080/17408989.2016.1241226); Dinan-Thompson and Penney (2015. “Assessment Literacy in Primary Physical Education.” European Physical Education Review 21 (4): 485–503. https://doi.org/10.1177/1356336X15584087)].Objectives: This article aims to describe: (1) the main difficulties encountered by PEH teachers regarding their assessment practices and (2) their opinions of the grading process in PEH.Method: An exploratory sequential mixed-method research design [Creswell (2014. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 4th ed. Thousand Oaks, CA: SAGE Publications] was used with PEH teachers to meet the objectives of this study. The first phase of the research, the qualitative phase, consisted of a 45-min semi-structured individual interview (n = 11) followed by a 60-min interview with the double [n = 10; Brau-Antony et al. (2014. “Curriculum en Éducation Physique et Sportive et évaluation certificative au baccalauréat.” Questions Vives 22:1–16. https://doi.org/10.4000/questionsvives.1596)]. The inductive content analyses [Creswell (2014. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 4th ed. Thousand Oaks, CA: SAGE Publications); L’Écuyer (1990. Méthodologie de l’analyse développementale de contenu: Méthode GPS et concept de soi. Québec: Presses de l’Université du Québec)] of these two interviews allowed us to develop the questionnaire used in the second phase of the research, the quantitative phase (n = 164). The data were exported into IBM SPSS statistics Version 27 software to make descriptive analyses (frequencies and means) and advanced statistical analyses (Chi-square test, Kruskall–Wallis test and two-tailed Kendall's Tau-B test).Findings: Three main difficulties to asessing in PEH are identified by teachers: (1) assessing in health education; (2) unclear ministerial expectations for assessment and (3) the cumbersome competency assessment process. Advanced statistical analyses also show that early-career teachers experience more difficulties. Two main opinions about grading also emerge from the data: (1) grading in PEH allows to see the progress of the students and (2) the lack of guidelines for grading makes it very subjective.Conclusion: In conclusion, this study contributes to existing knowledge about assessment in PEH from several perspectives. From a pre-service perspective, it seems imperative to address assessment with its challenges so that early-career teachers can adequately define their conception of assessment and its purposes. From a governmental and political perspective, it would be relevant for teachers to have clear guidelines for assessment. Finally, from a research perspective and to contribute to the improvement of assessment processes, we claim that there is an important need for future action research, which is to establish collaborations with PEH teachers to develop and experiment with simple and effective strategies to respond to certain difficulties arising from our results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".