ANALYZING AND MODELING STUDENTS¿ BEHAVIORAL DYNAMICS IN CONFIDENCE-BASED ASSESSMENT

Confidence-based assessment is a two-dimensional assessment paradigm which considers the confidence or expectancy level a student has about the answer, to ascertain his/her actual knowledge. Several researchers have discussed the usefulness of this model over the traditional one-dimensional assessment approach, which takes the number of correctly answered questions as a sole parameter to calculate the test scores of a student. Additionally, some educational psychologists and theorists have found that confidence-based assessment has a positive impact on students\u2019 academic performance, knowledge retention, and metacognitive abilities of self-regulation and engagement depicted during a learning process. However, to the best of our knowledge, these findings are not exploited by the educational data mining community, aiming to exploit students (logged) data to investigate their performance and behavioral characteristics in order to enhance their performance outcomes and/or learning experiences. Engagement reflects a student\u2019s active participation in an ongoing task or process, that becomes even more important when students are interacting with a computer-based learning or assessment system. There is some evidence that students\u2019 online engagement (which is estimated through their behaviors while interacting with a learning/assessment environment) is also positively correlated with good performance scores. However, no data mining method to date has measured students engagement behaviors during confidence-based assessment. This Ph.D. research work aimed to identify, analyze, model and predict students\u2019 dynamic behaviors triggered by their progression in a computer-based assessment system, offering confidence-driven questions. The data was collected from two experimental studies conducted with undergraduate students who solved a number of problems during confidence-based assessment. In this thesis, we first addressed the challenge of identifying different parameters representing students\u2019 problem-solving behaviors that are positively correlated with confidence-based assessment. Next, we developed a novel scheme to classify students\u2019 problem-solving activities into engaged or disengaged behaviors using the three previously identified parameters namely: students\u2019 response correctness, confidence level, feedback seeking/no-seeking behavior. Our next challenge was to exploit the students\u2019 interactions recorded at the micro-level, i.e. event by event, by the computer-based assessment tools, to estimate their intended engagement behaviors during the assessment. We also observed that traditional non-mixture, first-order Markov chain is inadequate to capture students\u2019 evolving behaviors revealed from their interactions with a computer-based learning/assessment system. We, therefore, investigated mixture Markov models to map students trails of performed activities. However, the quality of the resultant Markov chains is critically dependent on the initialization of the algorithm, which is usually performed randomly. We proposed a new approach for initializing the Expectation-Maximization algorithm for multivariate categorical data we called K-EM. Our method achieved better prediction accuracy and convergence rate in contrast to two pre-existing algorithms when applied on two real datasets. This doctoral research work contributes to elevate the existing states of the educational research (i.e. theoretical aspect) and the educational data mining area (i.e. empirical aspect). The outcomes of this work pave the way to a framework for an adaptive confidence-based assessment system, contributing to one of the central components of Adaptive Learning, that is, personalized student models. The adaptive system can exploit data generated in a confidence-based assessment system, to model students\u2019 behavioral profiles and provide personalized feedback to improve students\u2019 confidence accuracy and knowledge by considering their behavioral dynamics

A Prediction-Based Framework to Reduce Procrastination in Adaptive Learning Systems

Procrastination and other types of dilatory behaviour are common in online learning, especially in higher education. While procrastination is associated with worse performance and discomfort, positive forms of delay can be used as a deliberate strategy without any such consequences. Although dilatory behaviour has received attention in research, it has to my knowledge never been included as an integral part of an adaptive learning system. Differentiating between different types of delay within such a system would allow for tailored interventions to be provided in the future without alienating students who use delay as a successful strategy. In this thesis, I present four studies that provide the basis for such an endeavour. I first discuss the results of two studies that focussed on the prediction of the extent of dilatory behaviour in online assignments. The results of both studies revealed an advantage of objective predictors based on log data over subjective variables based on questionnaires. The predictive performance slightly improved when both sets of predictors were combined. In one of these studies, we implemented Bayesian multilevel models while the other aimed at comparing various machine learning algorithms to determine the best candidates for a future inclusion in real-time predictive models. The results reveal that the most suitable algorithm depended on the type of predictor, implying that multiple models should be implemented in the field, rather than selecting just one. I then present a framework for an adaptive learning system based on the other two studies, where I highlight how dilatory behaviour can be incorporated into such a system, in light of the previously discussed results. I conclude this thesis by providing an outlook into the necessary next steps before an adaptive learning system focussing on delay can be established

Jahrbuch der Schulentwicklung. Band 23. 50 Jahre Schulentwicklung - Leitthemen der empirischen Bildungsforschung

Das Jahrbuch der Schulentwicklung verbindet als Festschrift zum 50-jährigen Jubiläum des Instituts für Schulentwicklungsforschung (IFS) Themen der Bildungsforschung, die die Forschung des Instituts in der Vergangenheit und Gegenwart prägen. Thematisch wird dabei vor allem auf Schwerpunkte in den Bereichen sich wandelnder Lernbedingungen und sozialer Disparitäten, Schülerkompetenzen in den Domänen Lesen, Mathematik und politische Bildung sowie Einstellungen und Kompetenzen von Lehrkräften fokussiert. Gerahmt wird die Festschrift mit einem Interview zu Perspektiven auf die Forschungsleistung des Instituts. (DIPF/Orig.