A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems

ABSTRACT We present the TCS Alignment Toolbox, which offers a flexible framework to calculate and visualize (dis)similarities between sequences in the context of educational data mining and intelligent tutoring systems. The toolbox offers a variety of alignment algorithms, allows for complex input sequences comprised of multi-dimensional elements, and is adjustable via rich parameterization options, including mechanisms for an automatic adaptation based on given data. Our demo shows an example in which the alignment measure is adapted to distinguish students' Java programs w.r.t. different solution strategies, via a machine learning technique

Inside Out: Detecting Learners' Confusion to Improve Interactive Digital Learning Environments

Confusion is an emotion that is likely to occur while learning complex information. This emotion can be beneficial to learners in that it can foster engagement, leading to deeper understanding. However, if learners fail to resolve confusion, its effect can be detrimental to learning. Such detrimental learning experiences are particularly concerning within digital learning environments (DLEs), where a teacher is not physically present to monitor learner engagement and adapt the learning experience accordingly. However, with better information about a learner's emotion and behavior, it is possible to improve the design of interactive DLEs (IDLEs) not only in promoting productive confusion but also in preventing overwhelming confusion. This article reviews different methodological approaches for detecting confusion, such as self-report and behavioral and physiological measures, and discusses their implications within the theoretical framework of a zone of optimal confusion. The specificities of several methodologies and their potential application in IDLEs are discussed

Adaptive prototype-based dissimilarity learning

Zhu X. Adaptive prototype-based dissimilarity learning. Bielefeld: Universitätsbibliothek Bielefeld; 2015.In this thesis we focus on prototype-based learning techniques, namely three unsuper- vised techniques: generative topographic mapping (GTM), neural gas (NG) and affinity propagation (AP), and two supervised techniques: generalized learning vector quantiza- tion (GLVQ) and robust soft learning vector quantization (RSLVQ). We extend their abilities with respect to the following central aspects: • Applicability on dissimilarity data: Due to the increased complexity of data, in many cases data are only available in form of (dis)similarities which describe the relations between objects. Classical methods can not directly deal with this kind of data. For unsupervised methods this problem has been studied, here we transfer the same idea to the two supervised prototype-based techniques such that they can directly deal with dissimilarities without an explicit embedding into a vector space. • Quadratic complexity issue: For dealing with dissimilarity data, due to the need of the full dissimilarity matrix, the complexity becomes quadratic which is infeasible for large data sets. In this thesis we investigate two linear approximation techniques: Nyström approximation and patch processing, and integrate them into unsupervised and supervised prototype-based techniques. • Reliability of prototype-based classifiers: In practical applications, a relia- bility measure is beneficial for evaluating the classification quality expected by the end users. Here we adopt concepts from conformal prediction (CP), which provides point-wise confidence measure of the prediction, and we combine those with supervised prototype-based techniques. • Model complexity: By means of the confidence values provided by CP, the model complexity can be automatically adjusted by adding new prototypes to cover low confidence data space. • Extendability to semi-supervised problems: Besides its ability to evaluate a classifier, conformal prediction can also be considered as a classifier. This opens a way that supervised techniques can be easily extended for semi-supervised settings by means of a self-training approach

Metric Learning for Structured Data

Paaßen B. Metric Learning for Structured Data. Bielefeld: Universität Bielefeld; 2019.Distance measures form a backbone of machine learning and information retrieval in many application fields such as computer vision, natural language processing, and biology. However, general-purpose distances may fail to capture semantic particularities of a domain, leading to wrong inferences downstream. Motivated by such failures, the field of metric learning has emerged. Metric learning is concerned with learning a distance measure from data which pulls semantically similar data closer together and pushes semantically dissimilar data further apart. Over the past decades, metric learning approaches have yielded state-of-the-art results in many applications. Unfortunately, these successes are mostly limited to vectorial data, while metric learning for structured data remains a challenge. In this thesis, I present a metric learning scheme for a broad class of sequence edit distances which is compatible with any differentiable cost function, and a scalable, interpretable, and effective tree edit distance learning scheme, thus pushing the boundaries of metric learning for structured data. Furthermore, I make learned distances more useful by providing a novel algorithm to perform time series prediction solely based on distances, a novel algorithm to infer a structured datum from edit distances, and a novel algorithm to transfer a learned distance to a new domain using only little data and computation time. Finally, I apply these novel algorithms to two challenging application domains. First, I support students in intelligent tutoring systems. If a student gets stuck before completing a learning task, I predict how capable students would proceed in their situation and guide the student in that direction via edit hints. Second, I use transfer learning to counteract disturbances for bionic hand prostheses to make these prostheses more robust in patients' everyday lives

INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION

This Thesis describes our work at the boundary between Computer Science and Cognitive (Neuro)Science. In particular, (1) we have worked on methodological improvements to clustering-based meta-analysis of neuroimaging data, which is a technique that allows to collectively assess, in a quantitative way, activation peaks from several functional imaging studies, in order to extract the most robust results in the cognitive domain of interest. Hierarchical clustering is often used in this context, yet it is prone to the problem of non-uniqueness of the solution: a different permutation of the same input data might result in a different clustering result. In this Thesis, we propose a new version of hierarchical clustering that solves this problem. We also show the results of a meta-analysis, carried out using this algorithm, aimed at identifying specific cerebral circuits involved in single word reading. Moreover, (2) we describe preliminary work on a new connectionist model of single word reading, named the two-component model because it postulates a cascaded information flow from a more cognitive component that computes a distributed internal representation for the input word, to an articulatory component that translates this code into the corresponding sequence of phonemes. Output production is started when the internal code, which evolves in time, reaches a sufficient degree of clarity; this mechanism has been advanced as a possible explanation for behavioral effects consistently reported in the literature on reading, with a specific focus on the so called serial effects. This model is here discussed in its strength and weaknesses. Finally, (3) we have turned to consider how features that are typical of human cognition can inform the design of improved artificial agents; here, we have focused on modelling concepts inspired by emotion theory. A model of emotional interaction between artificial agents, based on probabilistic finite state automata, is presented: in this model, agents have personalities and attitudes that can change through the course of interaction (e.g. by reinforcement learning) to achieve autonomous adaptation to the interaction partner. Markov chain properties are then applied to derive reliable predictions of the outcome of an interaction. Taken together, these works show how the interplay between Cognitive Science and Computer Science can be fruitful, both for advancing our knowledge of the human brain and for designing more and more intelligent artificial systems

Analysis of Students' Programming Knowledge and Error Development

Programmieren zu lernen ist für viele eine große Herausforderung, da es unterschiedliche Fähigkeiten erfordert. Man muss nicht nur die Programmiersprache und deren Konzepte kennen, sondern es erfordert auch spezifisches Domänenwissen und eine gewisse Problemlösekompetenz. Wissen darüber, wie sich die Programmierkenntnisse Studierender entwickeln und welche Schwierigkeiten sie haben, kann dabei helfen, geeignete Lehrstrategien zu entwickeln. Durch die immer weiter steigenden Studierendenzahlen wird es jedoch zunehmend schwieriger für Lehrkräfte, die Bedürfnisse, Probleme und Schwierigkeiten der Studierenden zu erkennen. Das Ziel dieser Arbeit ist es, Einblick in die Entwicklung von Programmierkenntnissen der Studierenden anhand ihrer Lösungen zu Programmieraufgaben zu gewinnen. Wissen setzt sich aus sogenannten Wissenskomponenten zusammen. In dieser Arbeit fokussieren wir uns auf syntaktische Wissenskomponen, die aus abstrakten Syntaxbäumen abgeleitet werden können, und semantische Wissenskomponenten, die durch sogenannte Variablenrollen repräsentiert werden. Da Wissen an sich nicht direkt messbar ist, werden häufig Skill-Modelle verwendet, um den Kenntnissstand abzuschätzen. Jedoch hat die Programmierdomäne ihre eigenen speziellen Eigenschaften, die bei der Wahl eines geeigneten Skill-Modells berücksichtigt werden müssen. Eine der Haupteigenschaften in der Programmierung ist, dass die Wissenskomponenten nicht unabhängig voneinander sind. Aus diesem Grund schlagen wir ein dynamisches Bayesnetz (DBN) als Skill-Modell vor, da es erlaubt, diese Abhängigkeiten explizit zu modellieren. Neben derWahl eines passenden Skill-Modells, müssen auch bestimmte Meta-Parameter wie beispielsweise die Granularität der Wissenkomponenten festgelegt werden. Daher evaluieren wir, wie sich die Wahl von Meta-Parameters auf die Vorhersagequalität von Skill-Modellen auswirkt und wie diese Meta-Parameter gewählt werden sollten. Wir nutzen das DBN, um Lernkurven für jede Wissenskomponenten zu ermitteln und daraus Implikationen für die Lehre abzuleiten. Nicht nur das Wissen von Studierenden, sondern auch deren “Falsch”-Wissen ist von Bedeutung. Deswegen untersuchen wir zunächst manuell sämtliche Programmierfehler der Studierenden und bestimmen deren Häufigkeit, Dauer und Wiederkehrrate. Wir unterscheiden dabei zwischen den Fehlerkategorien syntaktisch, konzeptuell, strategisch, Nachlässigkeit, Fehlinterpretation und Domäne und schauen, wie sich die Fehler über die Zeit entwickeln. Außerdem verwenden wir k-means-Clustering um potentielle Muster in der Fehlerentwicklung zu finden. Die Ergebnisse unserer Fallstudien sind vielversprechend. Wir können zeigen, dass die Wahl der Meta-Parameter einen großen Einfluss auf die Vorhersagequalität von Modellen hat. Außerdem ist unser DBN vergleichbar leistungsstark wie andere Skill-Modelle, ist gleichzeitig aber besser zu interpretieren. Die Lernkurven der Wissenskomponenten und die Analyse der Programmierfehler liefern uns wertvolle Erkenntnisse, die der Kursverbesserung helfen können, z.B. dass die Studierenden mehr Übungsaufgaben benötigen oder mit welchen Konzepten sie Schwierigkeiten haben.Learning to program is a hard task since it involves different types of specialized knowledge. You do not only need knowledge about the programming language and its concepts, but also knowledge from the problem domain and general problem solving abilities. Knowing how students develop programming knowledge and where they struggle, may help in the development of suitable teaching strategies. However, the ever increasing number of students makes it more and more difficult for educators to identify students’ needs, problems, and deficiencies. The goal of the thesis is to gain insights into students programming knowledge development based on their solutions to programming exercises. Knowledge is composed of so called knowledge components (KCs). In this thesis, we focus on KCs on a syntactic level, which can be derived from abstract systax trees, e.g., loops, comparison, etc., and semantic level, represented by so called roles of variables. Since knowledge is not directly measurable, skill models are an often used for the estimation of knowledge. But, the programming domain has its own characteristics which have to be considered when selecting an appropriate skill model. One of the main characteristics of the programming domain are the dependencies between KCs. Hence, we propose and evaluate a Dynamic Bayesian Network (DBN) for skill modeling which allows to model that dependencies explicitly. Besides the choice of a concrete model, also certain metaparameters like, e.g., the granularity level of KCs, has to be set when designing a skill model. Therefore, we evaluate how meta-parameterization affects the prediction performance of skill models and which meta-parameters to choose. We use the DBN to create learning curves for each KC and deduce implications for teaching from them. But not only students knowledge but also their “mal-knowledge” is of importance. Therefore, we manually inspect students’ programming errors and determine the error’s frequency, duration, and re-occurrence. We distinguish between the error categories syntactic, conceptual, strategic, sloppiness, misinterpretation, and domain and analyze how the errors change over time. Moreover, we use k-means clustering to identify different patterns in the development of programming errors. The results of our case studies are promising. We show that the correct metaparameterization has a huge effect on the prediction performance of skill models. In addition, our DBN performs as well as the other skill models while providing better interpretability. The learning curves of KCs and the analysis of programming errors provide valuable information which can be used for course improvement, e.g., that students require more practice opportunities or are struggling with certain concepts.2022-02-0

Affective Computing

This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing