Väitös (tietojenkäsittelytiede): FM Erno Lokkila
Aika
16.2.2023 klo 12.00 - 16.00
FM Erno Lokkila esittää väitöskirjansa ”Understanding novice programmer behavior on introductory courses - Learning analytics approach” julkisesti tarkastettavaksi Turun yliopistossa torstaina 16.2.2023 klo 12 (Turun yliopisto, päärakennus, Tauno Nurmela -sali, Turku).
Vastaväittäjänä toimii apulaisprofessori Brett Becker (University College Dublin, Irlanti) ja kustoksena apulaisprofessori II vaihe Mikko-Jussi Laakso (Turun yliopisto). Tilaisuus on englanninkielinen. Väitöksen alana on tietojenkäsittelytiede.
Väitöskirja yliopiston julkaisuarkistossa: https://urn.fi/URN:ISBN:978-951-29-9148-8 (kopioi linkki selaimeen).
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Tiivistelmä väitöstutkimuksesta:
Technology offers many new possibilities for teaching. Learning analytics is one of these, and it focuses on analyzing, interpreting and utilizing the data generated by learners in improving teaching. In this thesis, I present a model which can be used to automatically identify novice programmer behavior.
The model consists of a state machine, which consists the possible states and state transitions possible by a student when doing an assignment. The student traverses through these states and all transitions are stored. Once the student finishes, the state transitions can be used to generate a Markov Model, which allows the mathematical modeling of the student. In the thesis, this model is used with machine learning algorithms to successfully cluster students into three distinct groups based on their behavior.
The data generated by the model can also be used to estimate the current skill level of the student programmer. The student’s learning can be tracked and with the data from the model, teachers can offer targeted help to students who need it, even on courses with hundreds or thousands of students. This research also paves the way for creating assignments that automatically adapt to the current skill level of the student.
Vastaväittäjänä toimii apulaisprofessori Brett Becker (University College Dublin, Irlanti) ja kustoksena apulaisprofessori II vaihe Mikko-Jussi Laakso (Turun yliopisto). Tilaisuus on englanninkielinen. Väitöksen alana on tietojenkäsittelytiede.
Väitöskirja yliopiston julkaisuarkistossa: https://urn.fi/URN:ISBN:978-951-29-9148-8 (kopioi linkki selaimeen).
***
Tiivistelmä väitöstutkimuksesta:
Technology offers many new possibilities for teaching. Learning analytics is one of these, and it focuses on analyzing, interpreting and utilizing the data generated by learners in improving teaching. In this thesis, I present a model which can be used to automatically identify novice programmer behavior.
The model consists of a state machine, which consists the possible states and state transitions possible by a student when doing an assignment. The student traverses through these states and all transitions are stored. Once the student finishes, the state transitions can be used to generate a Markov Model, which allows the mathematical modeling of the student. In the thesis, this model is used with machine learning algorithms to successfully cluster students into three distinct groups based on their behavior.
The data generated by the model can also be used to estimate the current skill level of the student programmer. The student’s learning can be tracked and with the data from the model, teachers can offer targeted help to students who need it, even on courses with hundreds or thousands of students. This research also paves the way for creating assignments that automatically adapt to the current skill level of the student.