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

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dc.contributor.authorStandar, Andreas
dc.contributor.authorLanda Vega, Dante
dc.contributor.departmentChalmers tekniska högskola / Institutionen för vetenskapens kommunikation och lärande (CLS)sv
dc.contributor.examinerBengmark, Samuel
dc.contributor.supervisorLundh, Torbjörn
dc.date.accessioned2020-06-24T21:26:51Z
dc.date.available2020-06-24T21:26:51Z
dc.date.issued2020sv
dc.date.submitted2020
dc.description.abstractWhen 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.sv
dc.identifier.coursecodeCLSX35sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/301005
dc.language.isoengsv
dc.setspec.uppsokHumanitiesTheology
dc.subjectcourse planningsv
dc.subjectoptimisationsv
dc.subjectgenetic algorithmssv
dc.subjectKolb’s learning stylessv
dc.subjectlesson classificationsv
dc.titleLesson Sequence Optimisation for a Group of Students with Different Learning Stylessv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc
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