Sequencing in Intelligent Tutoring Systems based on online learning Recommenders

In dieser Arbeit entwickeln und testen wir Algorithmen für Learning Analytics, die die personalisierte Sequenzierung von Matheaufgaben erlauben. Die Sequenzierung schlägt die nächste Aufgabe einem Schüler vor, die seine Lernbedürfnisse entspricht. Unsere Lösung basiert auf Vygotskys „Zone of Proximal Development“ (ZPD), das die weder zu einfachen noch zu schwierigen Aufgaben für den Schüler bestimmt. Der Sequenzer, auch Vygotsky Policy Sequencer genannt, ist in der Lage Aufgaben im ZPD zu erkennen, dank die von einem Vorhersagealgorithmus geschätzte zukünftige Leistung des Schülers. Die Arbeit enthält folgende Beiträge: (1) Die Evaluation der Anwendbarkeit von Matrix Factorization als Inhaltsdomäne unabhängige Algorithmus für die Vorhersage der Leistung der Schüler. (2) Anpassung und Evaluation eines Matrix Factorization basierenden Algorithmus, der die zeitliche Evolution der Schülerkenntnisse einbezieht. (3) Entwicklung von zwei Ansätzen für die Aktualisierung von Matrix Factorization basierenden Modellen durch den Kalman Filter. Zwei Aktualisierungsfunktionen sind benutzt: (a) eine einfache, die nur die letzte vom Schüler gesehene Aufgabe betrachtet, und (b) eine, die in der Lage ist, seine fehlenden Kompetenzen einzuschätzen. (4) Ein neues Verfahren von Machine Learning gesteuerte Sequenzer zu testen durch die Modellierung einer simulierten Umgebung, die aus simulierte Schülern und Aufgaben mit stetigen erzielten und gebrauchten Fähigkeiten und Schwierigkeitsgraden besteht. (5) Die Entwicklung einer minimal eingreifenden API für die leichte Integration von Machine Learning basierende Komponente in größere Systeme, um das Integrationsrisiko und die Kosten vom Know-How-Transfer zu minimieren. Dank all diesen Beiträgen, wurde der VPS in ein großes kommerzielles System integriert und mit 100 Kinder für einen Monat getestet. Der VPS zeigte Lerneffekte und wahrgenommene Erlebnisse, die mit den von den ITS Sequenzer vergleichbar sind. Infolge der besseren VPS Modellierfähigkeiten konnten die Schüler beendeten die Aufgaben schneller lösen.In this thesis we design and test Learning Analytics algorithms for personalized tasks' sequencing that suggests the next task to a student according to his/her specific needs. Our solution is based on a sequencing policy derived from the Vygotsky's Zone of Proximal Development (ZPD), which denes those tasks that are neither too easy not too dicult for the student. The sequencer, called Vygotsky Policy Sequencer (VPS), can identify tasks in the ZPD thanks to the information it receives from performance prediction algorithms able to estimate the knowledge of the student. Under this context we describe hereafter the thesis contributions. (1) A feasibility evaluation of domain independent Matrix Factorization applied in ITS for Performance Prediction. (2) An adaption and the related evaluation of a domain independent update for online learning Matrix Factorization in ITS. (3) A novel Matrix Factorization update method based on Kalman Filters approach. Two different updating functions are used: (a) a simple one considering the task just seen, and (b) one able to derive the skills' deficiency of the student. (4) A new method for offline testing of machine learning controlled sequencers by modeling simulated environment composed by a simulated students and tasks with continuous knowledge and score representation and different diffculty levels. (5) The design of a minimal invasive API for the lightweight integration of machine learning components in larger systems to minimize the risk of integration and the cost of expertise transfer. Profiting from all these contributions, the VPS was integrated in a commercial system and evaluated with 100 children over a month. The VPS showed comparable learning gains and perceived experience results with those of the ITS sequencer. Finally, thanks to its better modeling abilities, the students finish faster the assigned tasks

RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests

Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the Journal of Educational Data Minin

The role of machine learning in personalised instructional sequencing for language learning

The origins of personalised instructional sequencing can be dated back to the times of the Ancient Greeks to the times of Alexander The Great's tutor, Aristotle. However, over the centuries the demand for education and growth of students has been disproportionately greater than the number of teachers in training. Therefore, there has been a longstanding interest in finding a way to scale education without negatively affecting learning outcomes. This interest was fuelled further with the advent of computers and artificial intelligence, where a plethora of systems and models were built to bring technology driven personalised instructional sequencing to the world. Unfortunately, results were far from groundbreaking and many challenges still remain. In my thesis, I investigate three aspects of personalised instructional sequencing: the personalised instructional sequencing mechanism, the student knowledge representation, and human forgetting. While I do not cover the entirety of personalised instructional sequencing, I cover what I consider the foundational components. I link psychological theory to model selection and design in each of my systems and present experiments to illustrate their impact. I show how reinforcement learning can be used for vocabulary learning. I also present a model that uses neural collaborative filtering to learn student knowledge representations. Lastly, I present a state-of-the-art model to predict the probability of vocabulary word recall for students learning English as a second language. The system's novelty lies in the use of word complexity to adapt the forgetting curve as well as its incorporation of psychological theory to select an appropriate model

Advancement Auto-Assessment of Students Knowledge States from Natural Language Input

Knowledge Assessment is a key element in adaptive instructional systems and in particular in Intelligent Tutoring Systems because fully adaptive tutoring presupposes accurate assessment. However, this is a challenging research problem as numerous factors affect students’ knowledge state estimation such as the difficulty level of the problem, time spent in solving the problem, etc. In this research work, we tackle this research problem from three perspectives: assessing the prior knowledge of students, assessing the natural language short and long students’ responses, and knowledge tracing.Prior knowledge assessment is an important component of knowledge assessment as it facilitates the adaptation of the instruction from the very beginning, i.e., when the student starts interacting with the (computer) tutor. Grouping students into groups with similar mental models and patterns of prior level of knowledge allows the system to select the right level of scaffolding for each group of students. While not adapting instruction to each individual learner, the advantage of adapting to groups of students based on a limited number of prior knowledge levels has the advantage of decreasing the authoring costs of the tutoring system. To achieve this goal of identifying or clustering students based on their prior knowledge, we have employed effective clustering algorithms. Automatically assessing open-ended student responses is another challenging aspect of knowledge assessment in ITSs. In dialogue-based ITSs, the main interaction between the learner and the system is natural language dialogue in which students freely respond to various system prompts or initiate dialogue moves in mixed-initiative dialogue systems. Assessing freely generated student responses in such contexts is challenging as students can express the same idea in different ways owing to different individual style preferences and varied individual cognitive abilities. To address this challenging task, we have proposed several novel deep learning models as they are capable to capture rich high-level semantic features of text. Knowledge tracing (KT) is an important type of knowledge assessment which consists of tracking students’ mastery of knowledge over time and predicting their future performances. Despite the state-of-the-art results of deep learning in this task, it has many limitations. For instance, most of the proposed methods ignore pertinent information (e.g., Prior knowledge) that can enhance the knowledge tracing capability and performance. Working toward this objective, we have proposed a generic deep learning framework that accounts for the engagement level of students, the difficulty of questions and the semantics of the questions and uses a novel times series model called Temporal Convolutional Network for future performance prediction. The advanced auto-assessment methods presented in this dissertation should enable better ways to estimate learner’s knowledge states and in turn the adaptive scaffolding those systems can provide which in turn should lead to more effective tutoring and better learning gains for students. Furthermore, the proposed method should enable more scalable development and deployment of ITSs across topics and domains for the benefit of all learners of all ages and backgrounds

A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining

Educational Data Mining (EDM) has emerged as a vital field of research, which harnesses the power of computational techniques to analyze educational data. With the increasing complexity and diversity of educational data, Deep Learning techniques have shown significant advantages in addressing the challenges associated with analyzing and modeling this data. This survey aims to systematically review the state-of-the-art in EDM with Deep Learning. We begin by providing a brief introduction to EDM and Deep Learning, highlighting their relevance in the context of modern education. Next, we present a detailed review of Deep Learning techniques applied in four typical educational scenarios, including knowledge tracing, undesirable student detecting, performance prediction, and personalized recommendation. Furthermore, a comprehensive overview of public datasets and processing tools for EDM is provided. Finally, we point out emerging trends and future directions in this research area.Comment: 21 pages, 5 figure

Student Modeling in Intelligent Tutoring Systems

After decades of development, Intelligent Tutoring Systems (ITSs) have become a common learning environment for learners of various domains and academic levels. ITSs are computer systems designed to provide instruction and immediate feedback, which is customized to individual students, but without requiring the intervention of human instructors. All ITSs share the same goal: to provide tutorial services that support learning. Since learning is a very complex process, it is not surprising that a range of technologies and methodologies from different fields is employed. Student modeling is a pivotal technique used in ITSs. The model observes student behaviors in the tutor and creates a quantitative representation of student properties of interest necessary to customize instruction, to respond effectively, to engage students¡¯ interest and to promote learning. In this dissertation work, I focus on the following aspects of student modeling. Part I: Student Knowledge: Parameter Interpretation. Student modeling is widely used to obtain scientific insights about how people learn. Student models typically produce semantically meaningful parameter estimates, such as how quickly students learn a skill on average. Therefore, parameter estimates being interpretable and plausible is fundamental. My work includes automatically generating data-suggested Dirichlet priors for the Bayesian Knowledge Tracing model, in order to obtain more plausible parameter estimates. I also proposed, implemented, and evaluated an approach to generate multiple Dirichlet priors to improve parameter plausibility, accommodating the assumption that there are subsets of skills which students learn similarly. Part II: Student Performance: Student Performance Prediction. Accurately predicting student performance is one of the most desired features common evaluations for student modeling. for an ITS. The task, however, is very challenging, particularly in predicting a student¡¯s response on an individual problem in the tutor. I analyzed the components of two common student models to determine which aspects provide predictive power in classifying student performance. I found that modeling the student¡¯s overall knowledge led to improved predictive accuracy. I also presented an approach, which, rather than assuming students are drawn from a single distribution, modeled multiple distributions of student performances to improve the model¡¯s accuracy. Part III: Wheel-spinning: Student Future Failure in Mastery Learning. One drawback of the mastery learning framework is its possibility to leave a student stuck attempting to learn a skill he is unable to master. We refer to this phenomenon of students being given practice with no improvement as wheel-spinning. I analyzed student wheel-spinning across different tutoring systems and estimated the scope of the problem. To investigate the negative consequences of see what wheel-spinning could have done to students, I investigated the relationships between wheel-spinning and two other constructs of interest about students: efficiency of learning and ¡°gaming the system¡±. In addition, I designed a generic model of wheel-spinning, which uses features easily obtained by most ITSs. The model can be well generalized to unknown students with high accuracy classifying mastery and wheel-spinning problems. When used as a detector, the model can detect wheel-spinning in its early stage with satisfying satisfactory precision and recall

Automatic music transcription: challenges and future directions

Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects