The impact of high stakes oral performance assessment on students’ approaches to learning: a case study
Open accessAbstract
This paper presents findings from a case study on the impact of high stakes oral performance assessment on third year mathematics students’ approaches to learning (Entwistle and Ramsden, Understanding student learning, 1983). We choose oral performance assessment as this mode of assessment differs substantially from written exams for its dialogic nature and because variation of assessment methods is seen to be very important in an otherwise very uniform assessment diet. We found that students perceived the assessment to require conceptual understanding over memory and were more likely to employ revision strategies conducive to deep learning (akin to conceptual understanding) when preparing for the oral performance assessment than when preparing for a written exam. Moreover, they reported to have engaged and interacted in lectures more than they would have otherwise, another characteristic conducive to deep learning approaches. We conclude by suggesting some implications for the summative assessment of mathematics at university level.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Oral assessments provide authentic evaluation opportunities that align closely with professional workplace requirements.
“Furthermore, oral assessments provide authentic evaluation opportunities that align closely with professional workplace requirements (Iannone et al., 2020;Sotiriadou et al., 2020;Turjamaa et al., 2018)”
From Replacing Traditional Written Laboratory Reports with Interactive Oral Assessments: Enhancing Laboratory Skill Assessment in Undergraduate Genetics Education (2026) · cites this paper as Iannone et al., 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Working inside the Black Box: Assessment for Learning in the Classroom
Black, P., Harrison, C., Lee, C., Marshall, B., & Wiliam, D. (2004) · Phi Delta Kappan · doi:10.1177/003172170408600105 · Cited by 1,323 · see citing works
Students’ perceptions about evaluation and assessment in higher education: a review
Struyven, K., Dochy, F., & Janssens, S. (2005) · Assessment & Evaluation in Higher Education · doi:10.1080/02602930500099102 · Cited by 782 · see citing works
Flagged first, then by citations
Research works printed but not linked: 30
- Attride-Stirling, J. (2001). Thematic networks: an analytic tool for qualitative research. Qualitative Research, 1(3), 385-405.
- Bergqvist, E. (2007). Types of reasoning required in university exams in mathematics. The Journal of Mathematical Behavior, 26(4), 348-370.
- Birenbaum, M., Brueur, K., Cascallar, E., Dochy, F., Dori, Y., Ridgway, J., … Nickmans, G. (2006). A learning integrated assessment system. Educational Research Review, 1(1), 61-67.
- Boesen, J., Lithner, J., & Palm, T. (2010). The relation between types of assessment tasks and the mathematical reasoning students use. Educational Studies in Mathematics, 75(1), 89-105.
- Brown, G., Bull, J., & Pendlebury, M. (2013). Assessing student learning in higher education. Abingdon: Routledge.
- Darlington, E. (2014). Contrasts in mathematical challenges in A-level Mathematics and Further Mathematics, and undergraduate mathematics examinations. Teaching Mathematics and its Applications: An International Journal of the IMA, 33(4), 213-229.
- Entwistle, N. J., & Entwistle, A. (1991). Contrasting forms of understanding for degree examinations: The student experience and its implications. Higher Education, 22(3), 205-227.
- Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry, 12(2), 219-245.
- Gibbs, G., & Simpson, C. (2003). Measuring the response of students to assessment: The assessment experience questionnaire. In 11th Improving Student Learning Symposium, Hinckley (pp. 1-12).
- Gijbels, D., & Dochy, F. (2006). Students' assessment preferences and approaches to learning: Can formative assessment make a difference? Educational Studies, 32(4), 399-409.
- Gueudet, G. (2008). Investigating the secondary-tertiary transition. Educational Studies in Mathematics, 67(3), 237-254.
- Harlen, W., & Deakin Crick, R. (2003). Testing and motivation for learning. Assessment in Education: Principles, Policy & Practice, 10(2), 169-207.
- Iannone, P., & Simpson, A. (2011). The summative assessment diet: How we assess in mathematics degrees. Teaching Mathematics and its Applications: An International Journal of the IMA, 30(4), 186-196.
- Iannone, P., & Simpson, A. (2015). Students' views of oral performance assessment in mathematics: Straddling the 'assessment of' and 'assessment for' learning divide. Assessment & Evaluation in Higher Education, 40(7), 971-987.
- Joughin, G. (2010). A short guide to oral assessment. Leeds: Leeds Met Press in association with University of Wollongong.
- Kehm, B. M. (2001). Oral examinations at German universities. Assessment in Education: Principles, Policy & Practice, 8(1), 25-31.
- Lithner, J. (2004). Mathematical reasoning in calculus textbook exercises. The Journal of Mathematical Behavior, 23(4), 405-427.
- Lithner, J. (2008). A research framework for creative and imitative reasoning. Educational Studies in Mathematics, 67(3), 255-276.
- Mac an Bhaird, C., Nolan, B. C., O'Shea, A., & Pfeiffer, K. (2017). A study of creative reasoning opportunities in assessments in undergraduate calculus courses. Research in Mathematics Education, 19(2), 147-162.
- Marton F., & R. Säljö. (1997). Approaches to learning. In F. Marton, D. Hounsell and N. Entwistle (Eds.) The experience of learning. Implications for teaching and studying in higher education (pp. 36-55). Edinburgh, UK: Scottish Academic Press.
- Moore, S., Armstrong, C., & Pearson, J. (2008). Lecture absenteeism among students in higher education: A valuable route to understanding student motivation. Journal of Higher Education Policy and Management, 30(1), 15-24.
- Nash, G., Crimmins, G., & Oprescu, F. (2016). If first-year students are afraid of public speaking assessments what can teachers do to alleviate such anxiety? Assessment & Evaluation in Higher Education, 41(4), 586-600.
- Newman-Ford, L., Fitzgibbon, K., Lloyd, S., & Thomas, S. (2008). A large-scale investigation into the relationship between attendance and attainment: A study using an innovative, electronic attendance moni- toring system. Studies in Higher Education, 33(6), 699-717.
- Odafe, V. U. (2006). Oral examination in college mathematics: An alternative assessment technique. Problems, Resources, and Issues in Mathematics Undergraduate Studies, 16(3), 243-256.
- Schoultz, J., Säljö, R., & Wyndhamn, J. (2001). Conceptual knowledge in talk and text: What does it take to understand a science question? Instructional Science, 29(3), 213-236.
- Scouller, K. (1998). The influence of assessment method on students' learning approaches: Multiple choice question examination versus assignment essay. Higher Education, 35(4), 453-472.
- Skemp, R. (1976). Relational understanding and instrumental understanding. Mathematics Teaching, 77(1), 20-26.
- Stake, R. E. (2000). Qualitative case studies. In N. K. Denzin, & S. Y. Lincoln (Eds.), Handbook of qualitative research (pp. 443-466). London, UK: Sage.
- Stylianides, A. J. (2019). Secondary students' proof constructions in mathematics: The role of written versus oral mode of argument representation. Review of Education, 7(1), 156-182.
- Videnovic, M. (2017b). Evidential vs non-evidential beliefs in case of oral assessment: A sequel. Open Mathematical Education Notes, 7(2), 43-47.
Cited by
Sorted by citations
Della Vedova, C. B., Birbeck, D., & Davey, S. K. (2026) · Research in Science Education · doi:10.1007/s11165-026-10380-3
- Authors
- Iannone, P., Czichowsky, C., & Ruf, J.
- Affiliations
- Loughborough University (GB)
- London School of Economics and Political Science (GB)
- Corresponding author
- Iannone, Paola
- Venue
- Educational Studies in Mathematics
- Published
- 2020-03
- Volume
- 103
- Issue
- 3
- Pages
- 313-337
- ISSN
- 0013-1954 (ISSN-L)
- 1573-0816
- OpenAlex
- W3004235165
- Cited by
- 42
- References
- 2
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 10 · 2023: 12 · 2022: 2 · 2021: 6 · 2020: 2
- FWCI
- 8.35
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Educational Studies in Mathematics (publishedVersion, cc-by)
- London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion)
- London School of Economics and Political Science Theses Online (London School of Economics and Political Science) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Funders
- London School of Economics and Political Science
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Education › Evaluation of Teaching Practices
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematics Education and Programs
- Keywords
- high-stakes testing, oral performance assessment, learning methods, deep learning, surface learning, mathematics students, summative assessment, exam preparation strategies, conceptual understanding, student perceptions of assessment, university mathematics education
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
