Leveraging Influential Factors into Bayesian Knowledge Tracing

Predicting student performance is an important part of the student modeling task in Intelligent Tutoring System (ITS). The state-of-art model for predicting student performance - Bayesian Knowledge Tracing (KT) has many critical limitations. One specific limitation is that KT has no underlying mechanism for memory decay represented in the model, which means that no forgetting is happening in the learning process. In addition we notice that numerous modification to the KT model have been proposed and evaluated, however many of these are often based on a combination of intuition and experience in the domain, leading to models without performance improvement. Moreover, KT is computationally expensive, model fitting procedures can take hours or days to run on large datasets. The goal of this research work is to improve the accuracy of student performance prediction by incorporating the memory decay factor which the standard Bayesian Knowledge Tracing had ignored. We also propose a completely data driven and inexpensive approach to model improvement. This alternative allows for researchers to evaluate which aspects of a model are most likely to result in model performance improvements based purely on the dataset features that are computed from ITS system logs

Know-how, intellectualism, and memory systems

ABSTRACTA longstanding tradition in philosophy distinguishes between knowthatand know-how. This traditional “anti-intellectualist” view is soentrenched in folk psychology that it is often invoked in supportof an allegedly equivalent distinction between explicit and implicitmemory, derived from the so-called “standard model of memory.”In the last two decades, the received philosophical view has beenchallenged by an “intellectualist” view of know-how. Surprisingly, defenders of the anti-intellectualist view have turned to the cognitivescience of memory, and to the standard model in particular, todefend their view. Here, I argue that this strategy is a mistake. As it turns out, upon closer scrutiny, the evidence from cognitivepsychology and neuroscience of memory does not support theanti-intellectualist approach, mainly because the standard modelof memory is likely wrong. However, this need not be interpretedas good news for the intellectualist, for it is not clear that theempirical evidence necessarily supp..

Nasal Morphology As A Predictor Of Craniofacial Growth Direction

It is important for an orthodontist to predict growth related changes and thereby appropriately time orthodontic treatment using the vertical indicators currently available. A potential predictor of craniofacial growth direction that has been discussed yet remains scientifically unexplored is nasal morphology. The objectives of this study are to determine if a difference in pre-adolescent nasal contour exists between post-adolescent normodivergent and hyperdivergent subjects, and if nasal contour morphology in pre-adolescent females is a reliable indicator of future craniofacial growth direction. A significant difference in pre-adolescent nasal contour morphology was found between normodivergent and hyperdivergent groups. A pre-adolescent nasal contour elevation \u3e0.75mm may be indicative of future vertical craniofacial growth direction. However, pre-adolescent nasal contour morphology was judged to be a fair-to-poor diagnostic indicator of future craniofacial growth direction and should not be relied upon in craniofacial growth direction predictions

last night\u27s mouth

last night\u27s mouth is a narrative collection of prose poems chronicling the journey from break-up to new love. The manuscript employs overarching metaphors of math and music to communicate the attempts of the protagonist, Cassie, to rationalize emotional situations, while list poems emulate those struggles formulaically. Frequent references to popular culture contemporize her relationships. Rhythmic lines and scrambled syntax express the sense of poetry in prosaic paragraphs

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

Learning-by-Doing, Organizational Forgetting, and Industry Dynamics

Learning-by-doing and organizational forgetting are empirically important in a variety of industrial settings. This paper provides a general model of dynamic competition that accounts for these fundamentals and shows how they shape industry structure and dynamics. We show that forgetting does not simply negate learning. Rather, they are distinct economic forces that interact in subtle ways to produce a great variety of pricing behaviors and industry dynamics. In particular, a model with learning and forgetting can give rise to aggressive pricing behavior, varying degrees of long-run industry concentration ranging from moderate leadership to absolute dominance, and multiple equilibria

Building Intelligent Tutoring Systems

This project\u27s goal was to improve the ASSISTments intelligent tutoring system\u27s algebraic capabilities. We worked towards three main objectives. First, we built support for parsing expressions and comparing them for algebraic equality. Second, we implemented an interactive grapher capable of plotting a variety of expressions. Third, we added support for rendering expressions to well formatted images. Finally, we implemented a basic tutoring system including sample problems that demonstrate our work, establishing our tools\u27 usability and integrability

Purpose-first Programming: A Programming Learning Approach for Learners Who Care Most About What Code Achieves

Introductory programming courses typically focus on building generalizable programming knowledge by focusing on a language’s syntax and semantics. Assignments often involve “code tracing” problems, where students perform close tracking of code’s execution, typically in the context of ‘toy’ problems. “Reading-first” approaches propose that code tracing should be taught early to novice programmers, even before they have the opportunity to write code. However, many learners do not perform code tracing, even in situations when it is helpful for other students. To learn more, I talked to novice programmers about their decisions to trace and not trace code. Through these studies, I identified both cognitive and affective factors related to learners’ motivation to trace. My research found that tracing activities can create a “perfect storm” for discouraging learners from completing them: they require high cognitive load, leading to a low expectation of success, while also being disconnected from meaningful code, resulting in low value for the task. These findings suggest that a new learning approach, where novices quickly and easily create or understand useful code without the need for deep knowledge of semantics, may lead to higher engagement. Many learners may not care about exactly how a programming language works, but they do care about what code can achieve for them. I drew on cognitive science and theories of motivation to describe a “purpose-first” programming pedagogy that supports novices in learning common code patterns in a particular domain. I developed a proof-of-concept ”purpose-first” programming curriculum using this method and evaluated it with non-major novice programmers who had a variety of future goals. Participants were able to complete scaffolded code writing, debugging, and explanation activities in a new domain (web scraping with BeautifulSoup) after a half hour of instruction. An analysis of the participants’ thinkalouds provided evidence the learners were thinking in terms of the patterns and goals that they learned with in the purpose-first curriculum. Overall, I found that these novices were motivated to continue learning with purpose-first programming. I found that these novices felt successful during purpose-first programming because they could understand and complete tasks. Novices perceived a lower cognitive load on purpose-first programming activities than many other typical learning activities, because, in their view, plans helped them apply knowledge and focus only on the most relevant information. Participants felt that what they were learning was applicable, and that the curriculum provided conceptual, high-level knowledge. For some participants, particularly conversational programmers who didn’t plan to program in their careers, this information was sufficient for their needs. Other participants felt that purpose-first programming was a starting point, from which they could move forward to gain a deeper understanding of how code works.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167912/1/kicunn_1.pd