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Assessing Student Self-Assessment: An Additional Argument for Blended Learning - FR Arico
The document discusses a study on student self-assessment within blended learning environments, focusing on the relationship between academic self-efficacy, self-assessment skills, and attainment in seminars and workshops. Key findings indicate that higher attaining students exhibit greater confidence in their abilities, while lower attaining students struggle with self-assessment, potentially influenced by the learning environment. Further research is proposed to delve deeper into the effects of confidence evaluation and learning analytics on student performance.
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Assessing Student Self-Assessment: An Additional Argument for Blended Learning - FR Arico
- 1. 1 Assessing Student Self-Assessment: AnAdditional Argument for Blended Learning Fabio R. Aricò Duncan Watson Chris Thomson NRHE SRHE – Dec 2014
- 2. ACKNOWLEDGEMENTS UEA-HEFCE Widening ParticipationTeaching Fellowship HEA – Teaching Development Grant Scheme 2
- 3. OUTLINE 1. Overview ofthe “When Student Confidence Clicks Project” 2. Introduction to 2 learning environments to be compared: seminars and workshops Learning analytics: the relationship between attainment & self-assessment 4. Final remarks: assessing self-assessment 5. Further research 3
- 4. ETHICAL REMARK You willbe presented with data collected during teaching sessions. Students involved have given informed consent for me to analyse their responses and present the results of this analysis. I can assist with ethical queries as well, please ask me. 4
- 5. 1. When StudentConfidence Clicks: the role of Academic Self-Efficacy in Learning 5
- 6. PROJECT DEVELOPMENTS Project Resources:https://sites.google.com/site/fabioarico/hea_tdg Project Workshop: Wednesday 3 Sep 2014 - University of East Anglia 6
- 7. BLENDED LEARNING ENVIRONMENT •Intense use of Student Response Systems (clickers). • Focus on increasing students’ Academic Self-Efficacy beliefs Self-Assessment is a fundamental component. • Intense use of SRS + VLE dialogue with the students – closing feedback loop blended learning, but also blended surveying! • Learning analytics to disentangle relationship between different aspects of the learning experience. 7
- 8. DATASETS Student Q1 Q2Q3 … 1 0 1 1 2 1 0 0 3 1 1 … … performance per question confidence by question performanceperstudent confidencebystudent 8 longitudinal study - across all lectures - across all seminars - across all workshops Intermediate and final attainment outcomes - course test - final exam
- 9.
- 10. 2. Workshops andSeminars Attainment & Self-Assessment 10
- 11. MOTIVATION and COREIDEAS • This is a by-product of my HEA TDG project, which is taking life on its own Self-Assessment as an important determinant of Self-Efficacy beliefs (Bandura, 1977 – Pajares, 1996) • Self-Assessment skills contribute to form autonomous students HE learning and teaching practice should develop these (Boud, 1995 – Boud and Falchikov, 2006/7 – Evans, 2013) • Empirical evidence seems to identify positive association between self-assessment skills and performance in academic settings Dunning et.al. (2003), Zell & Krizan (2014) • Challenge: does (self)assessment design have a role to play? 11
- 12. TEACHING PROTOCOL –the module Introductory Macroeconomics Level 1 – compulsory year-long module - 170 students Lectures traditional frontal-teaching (10 per sem.) Seminars small group, pre-assigned problem sets (4 per sem.) Workshops large group, problem-solving sessions (4 per sem.) Support Sessions non-compulsory drop-in sessions (4 per sem.) 12
- 13. WORKSHOPS – learningenvironment • Large flipped-class environment (2 Groups - 85 students each) • 1st part: Multiple choice questions (4 choices) - 2nd part: problem-solving • Iterated algorithm: 1. Ask Question 1st round 4 choices (distribution not revealed) 2. Ask Self-Assessment Question 4 levels Likert-scale (strongly/agree – strongly/disagree) 3. Peer-Instruction 4. Ask Question 2nd round show answers – teach correct answer and give explanations. 13
- 14. WORKSHOPS – datacoding For each session (7 in a year): • Code 1st response: 1 = correct 0 = incorrect • Code confidence in response: 1 = strongly/agree 0 = strongly/disagree • Compute average score and average confidence per student. attainment self-assessment measure measure 14
- 15. WORKSHOPS – datacoding For each student (in each session): If student average score > session average score high-attainment otherwise low-attainment METHOD A: If student average conf. > session average conf. high-confidence otherwise low-confidence METHOD B: If student average conf. > 0.5 high-confidence otherwise low-confidence 15
- 16. WORKSHOPS – dataanalysis Cross-tabulate results: 16
- 17. WORKSHOPS - results Whatis the relationship between attainment and confidence? 17
- 18. WORKSHOP - results Whatis the relationship between attainment and confidence? 18
- 19. SEMINARS – learningenvironment • Small-class environment (Groups of 13-15 students each) • 1st part: Multiple choice quiz (4 choices) - 2nd part: problem-solving • Structure of the Quiz: - Paper based - 5-6 minutes duration - 3 or 4 formative questions - 1 self-assessment question on the quiz - 1 self-efficacy question about future beliefs. 19
- 20. SEMINARS – datacoding For each session (7 in a year): • Code each answer: 1 = correct 0 = incorrect • Code confidence in response: 1 = strongly/agree 0 = strongly/disagree • Compute average score attainment measure There is just one confidence response confidence measure 20
- 21. SEMINARS – datacoding For each student (in each session): If student average score > session average score high-attainment otherwise low-attainment If student confidence = 1 high-confidence otherwise low-confidence Note the asymmetry with workshops. This was addressed this year, asking about confidence question by question. 21
- 22. SEMINARS - results Whatis the relationship between attainment and confidence? 22
- 23. SEMINARS - results Whatis the relationship between attainment and confidence? 23
- 24. 3. Final Remarks AssessingSelf-Assessment 24
- 25. PREVIOUS LITERATURE • Inliterature there is a prevailing idea: low-attainment students are not able to self-assess their performance. • Dunning, D., Johnson, K., Ehrlinger, J., and Kruger, J., (2003), “Why People Fail to Recognise their Own Incompetence”, Current Directions in Psychological Science, 12:83, 83-87. • But the results find in my research seem to question the learning environment within which self-assessment is being observed. the learning environment affects the relationship attainment-confidence. 25
- 26. OVERALL RESULTS • InSeminar Quizzes: high-attainment students display higher confidence low-attainment students not able to self-assess their performance. • In Workshop sessions: high-attainment students display higher confidence low-attainment students display lower confidence. • How to interpret this asymmetry? 26
- 27. OVERALL RESULTS • InSeminar Quizzes: 3 or 4 questions, paper-based quiz, 5-6 minutes, not anonymous, small class 1 confidence assessment for overall performance. • In Workshop sessions: 5-10 questions, clicker response, slower pace, quasi-anonymous, large class 1 confidence assessment for each question asked. 27
- 28. OVERALL RESULTS • Low-attainmentstudents encounter more difficulties in self-assessing their performance in an environment where: they self-assess their ‘overall’ performance on a composite task they are exposed to questions for a shorter period of time they are exposed to fewer questions, not anonymously. • Focus group interviews (differentiated by attainment groups) confirm that low-attainment students display poorer self-assessment skills. • So what is happening in workshops? 28
- 29.
- 30. FURTHER RESEARCH • Introduceconfidence evaluation question-by-question in Seminars. • Explore the relationship between objective and subjective evaluation relationship between response entropy and student confidence. • “Peer-Instruction Unveiled” relationship between 1st and 2nd response – ‘learning gains’ relationship between learning gains and student confidence. • Learning analytics at student-level investigate the role of demographics investigate the impact of formative assessment on summative assessment. 30