Establishing the Correlation between Identifying Slow and Advanced learners Scale on Regular Academics among Undergraduate Student
DOI:
https://doi.org/10.37506/9c44fv20Keywords:
slow and advanced learner,identifying slow and advanced learner scale, reliability, correlation .Abstract
Purpose: Education is changing drastically with innovation impacting it the most. Bringing changes in the constantly changing world is important. This can only occur when we identify and categorizeour students into various types of learners, which can help us correctly deliver the knowledge. This was achieved by using the traditional method of categorizing the scores of students from the previous exams they have taken for example their 12th marks or previous years result. This method does not contain other domainsthat can focus on areasthat can improve the overall score of the student as a learner. The study aimed to find the reliability and correlation to effectively identify slow and advanced learners.
Methods: The study was conducted at a health university. First-year BPT undergraduate students were selected to identify slow and advanced learners. Data collection was carried out by a subject teacher appointed to assess students during class sessions. Students were evaluated per lecture, using the tool over the whole academic year.
Results: The overall scale demonstrates strong internal consistency with a Cronbach's alpha of 0.9307. While most items contribute positively to the scale's reliability. The correlations between total scores and other scores show that higher academic performance in these areas is positively associated with total scores.
Conclusions: The tool demonstrates strong reliability and validity in identifying slow and advanced learners, making it a valuable resource for educators aiming to enhance student learning outcomes.
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References
Tomlinson CA, Moon TS. Assessment and student success in a differentiated classroom. Alexandria (VA): ASCD; 2013.
Sternberg, R. J., & Grigorenko, E. L.Practical Intelligence and the Role of Traditional Intelligence in Education.Educational Psychologist, 2004; 39(1), 1-10.
Reis, S. M., & Renzulli, J. S.The Schoolwide Cluster Grouping Model: A Practical Strategy for Differentiating Instruction and Improving Student Learning.Gifted Child Quarterly, 2010; 54(4), 263-274.
Boudah, D. J.The Use of Curriculum-Based Measurement to Identify and Support Struggling Readers.Journal of Learning Disabilities, 2006; 39(2), 148-159.
Joffe V, Duffield L. Understanding the needs of students who deviate from the norm: A review of current practices and strategies for improvement.J Educ Psychol.2022;114(3):498-512. doi:10.1037/edu0000452.
Fuchs, D., & Fuchs, L. S.Assessment-Based Instruction: Developing Tools to Identify and Address Diverse Learning Needs.J Learn Disabil. 2017;50(5):494-506. doi:10.1177/0022219416633591.
Hattie, J.Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement.Routledge; 2009.
Siemens, G.Learning Analytics: The Emergence of a Discipline.European Journal of Education.2013;48(3):442-448. doi:10.1111/ejed.12014.
Hall T, Vue G, & Palmer D. Differentiated instruction and the role of formative assessment.J Spec Educ.2018;52(4):206-215. doi:10.1177/0022466918771964.
Tavakol M, Dennick R. Making sense of Cronbach's alpha. International Journal of Medical Education. 2011;2:53-55. doi:10.5116/ijme.4dfb.8dfd.
Field A. Discovering Statistics Using SPSS. 5th ed. London: SAGE Publications; 2018.
Reddy M, Sulaiman A. Development and validation of an academic performance assessment scale for university students. Journal of Educational Psychology. 2021;113(4):512-523. doi:10.1037/edu0000461.
Gendal H, Bowers A. Gender differences in academic performance: A longitudinal study in higher education. Educational Research and Reviews. 2020;15(6):321-329. doi:10.5897/ERR2019.3898.
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