Lesson Sequence Optimisation for a Group of Students with Different Learning Styles

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/301005
Download file(s):
File Description SizeFormat 
annotated-Lesson_Sequence_Optimisation_final_v2.pdf10.38 MBAdobe PDFThumbnail
View/Open
Bibliographical item details
FieldValue
Type: Examensarbete för masterexamen
Title: Lesson Sequence Optimisation for a Group of Students with Different Learning Styles
Authors: Standar, Andreas
Landa Vega, Dante
Abstract: When teachers are planning courses there is difficulty in knowing whether or not the course will evoke engagement among the students. This work aims to investigate the possibility of using mathematics, computer science, and pedagogical models to create a lesson sequence optimisation model that can simulate the engagement of a class of students with different preferences when it comes to the lessons they prefer. The model that was developed is solved using evolutionary algorithms, a biologically inspired optimisation method often used to optimise problems with complicated solution spaces. To facilitate this, data was collected from students in upper secondary school, studying the technology programme in the city of Gothenburg with vicinity (N=104) regarding their learning style preferences from Kolb’s Experiential learning theory as well as their lesson type preferences according to a lesson classification model developed for this purpose. Investigations were made to see if there was a correlation between learning style and lesson type preferences in the students, which analysis using Kendall’s rank correlation coefficient disproved. However, the data collected indicates that the population prefers lessons where they receive direct instructions and are working alone. Moreover, simulations were made using the data from the students for eight different optimisation norms. From the simulations an optimal and unique lesson sequence was found to maximise the students’ engagement in the course. From the other optimisation norms the conclusion was drawn that the least engaged student in the class fluctuates the most with different optimisation norms. Thus from the different optimisation norms the conclusion was drawn that a good strategy for a teacher is to focus on the least engaged students.
Keywords: course planning;optimisation;genetic algorithms;Kolb’s learning styles;lesson classification
Issue Date: 2020
Publisher: Chalmers tekniska högskola / Institutionen för vetenskapens kommunikation och lärande (CLS)
URI: https://hdl.handle.net/20.500.12380/301005
Collection:Examensarbeten för masterexamen // Master Theses



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.