Knowledge Tracing: A Review of Available Technologies

As a student modeling technique, knowledge tracing is widely used by various intelligent tutoring systems to infer and trace the individual’s knowledge state during the learning process. In recent years, various models were proposed to get accurate and easy-to-interpret results. To make sense of the wide Knowledge tracing (KT) modeling landscape, this paper conducts a systematic review to provide a detailed and nuanced discussion of relevant KT techniques from the perspective of assumptions, data, and algorithms. The results show that most existing KT models consider only a fragment of the assumptions that relate to the knowledge components within items and student’s cognitive process. Almost all types of KT models take “quize data” as input, although it is insufficient to reflect a clear picture of students’ learning process. Dynamic Bayesian network, logistic regression and deep learning are the main algorithms used by various knowledge tracing models. Some open issues are identified based on the analytics of the reviewed works and discussed potential future research directions

Evaluation of topic-based adaptation and student modeling in QuizGuide

This paper presents an in-depth analysis of a nonconventional topic-based personalization approach for adaptive educational systems (AES) that we have explored for a number of years in the context of university programming courses. With this approach both student modeling and adaptation are based on coarse-grained knowledge units that we called topics. Our motivation for the topic-based personalization was to enhance AES transparency for both teachers and students by utilizing typical topic-based course structures as the foundation for designing all aspects of an AES from the domain model to the end-user interface. We illustrate the details of the topic-based personalization technology, with the help of the Web-based educational service QuizGuide—the first system to implement it. QuizGuide applies the topic-based personalization to guide students to the right learning material in the context of an undergraduate C programming course. While having a number of architectural and practical advantages, the suggested coarse-grained personalization approach deviates from the common practices toward knowledge modeling in AES. Therefore, we believe that several aspects of QuizGuide required a detailed evaluation—from modeling accuracy to the effectiveness of adaptation. The paper discusses how this new student modeling approach can be evaluated, and presents our attempts to evaluate it from multiple different prospects. The evaluation of QuizGuide across several consecutive semesters demonstrates that, although topics do not always support precise user modeling, they can provide a basis for successful personalization in AESs

Online Knowledge Sharing: Investigating the Community of Inquiry Framework and Its Effect on Knowledge Sharing Behavior in Online Learning Environments

The purpose of this study was to examine whether the CoI framework can predict self-reported knowledge sharing behaviors within graduate-level online courses. The overall goal was to determine if high levels of social, teaching, and cognitive presence can lead to increased knowledge distribution within online learning environments, leading to the co-construction of knowledge among learners. As part of the study, graduate students from the field of education were asked to complete a survey, which combined Swan et al.\u27s (2008) CoI survey instrument and Yu, Lu, & Liu\u27s (2010) knowledge sharing survey tool. The survey assessed students\u27 perceptions of social, teaching, and cognitive presence within their respective online courses, and also measured their knowledge sharing behavior within these same courses. The independent variables were totaled scores of social presence, teaching presence, and cognitive presence. The dependent variable was the totaled score of knowledge sharing behavior. A standard multiple regression design was utilized to determine whether the independent variables (social presence, teaching presence, and cognitive presence) are predictors of the dependent variable (knowledge sharing behavior). Regression results indicated that an overall model with two independent variables - teaching presence and social presence - significantly predicts knowledge sharing behavior, R2 = .637, R2adj=.615, F(2, 33) = 29.001, p \u3c .001. Cognitive presence, however, was not shown to significantly contribute to this model. In line with existing theories - including social capital theory, the organization knowledge creation theory (OKCT), and self-determination theory - results suggest that the more social elements of the CoI framework might better motivate students to interact and share knowledge. On the other hand, cognitive presence, which is more closely tied to individual learning outcomes, plays a smaller role in motivating students to participate and share knowledge within online learning environments

Models and Algorithms for Understanding and Supporting Learning Goals in Information Retrieval

While search technology is widely used for learning-oriented information needs, the results provided by popular services such as Web search engines are optimized primarily for generic relevance, not effective learning outcomes. As a result, the typical information trail that a user must follow while searching to achieve a learning goal may be an inefficient one, possibly involving unnecessarily difficult content, or material that is irrelevant to actual learning progress relative to a user's existing knowledge. My work addresses these problems through multiple studies where various models and frameworks are developed and tested to support particular dimensions of search as learning. Empirical analysis of these studies through user studies demonstrate promising results and provide a solid foundation for further work. The earliest work we focused on centered on developing a framework and algorithms to support vocabulary learning objectives in a Web document context. The proposed framework incorporates user information, topic information and effort constraints to provide a desirable combination of personalized and efficient (by word length) learning experience. Our user studies demonstrate the effectiveness of our framework against a strong commercial baseline's (Google search) results in both short- and long-term assessment. While topic-specific content features (such as frequency of subtopic occurrences) naturally play a role in influencing learning outcomes, stylistic and structural features of the documents themselves may also play a role. Using such features we construct robust regression models that show strong predictive strength for multiple measures of learning outcomes. We also show early evidence that regression models trained on one dataset of search as learning can show strong test-set predictions on an independent dataset of search as learning, suggesting a certain degree of generalizability of stylistic content features. The models developed in my work are designed to be as generalizable, scalable and efficient as possible to make it easier for practitioners in the field to improve how people use search engines for learning. Finally, we investigate how gaze-tracking and automatic question generation could be used to scale a form of active learning to arbitrary text material. Our results show promising potential for incorporating interactive learning experiences in arbitrary text documents on the Web. A major theme in these studies centers on understanding and improving how people learn when using Web search engines. We also put specific emphasis on long-term learning outcomes and demonstrate that our models and frameworks actually yield sustainable knowledge gains, both for passive and interactive learning. Taken together, these research studies provide a solid foundation for multiple promising directions in exploring search as learning.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155065/1/rmsyed_1.pd

Determinants that impact first year male students’ motivation to learn at UAE public colleges

UAE male undergraduate dropout is a bleed into the country’s human resources in its strategic quest to enter the digital economy with a well-educated local generation in a post oil-era. The phenomenon is more apparent in first year students studying foundation courses of English to be prepared to enter college. As students enter the learning environment of higher education institutes, they move from teacher-centric education to learner-centric education, from a predominantly Arab culture of high school, to a predominantly Western environment of colleges where challenges of adaptability arise. In these socially and academically changing education communities, students are expected to assume personal responsibilities in a learner-centric environment utilising different teaching methods and aiding technologies than what they were used to at high school. Inabilities to adapt to this environment have challenging effects on students’ motivation leading to unsatisfactory academic results and even dropouts. This qualitative descriptive research was conducted using 13 focus groups of first year undergraduate UAE males in the three public UAE colleges of United Arab Emirates University (UAEU), Zayed University (ZU), and Higher Colleges of Technology (HCT). The aim was to understand student perception of their social and education environment and its elements that affect their motivation to learn. The results show that social, curriculum, personal and college related elements play important roles on students’ motivation and engagement in learning. A theoretical contribution of the study is that it adds evidence that focus groups can be used as a self-contained research method. A practical contribution of the research is that it presented a detailed account of elements that have positive and negative impact on motivation, and the suggested remedies for higher education policy makers, administrators and instructors to implement and improve student retention. A side contribution of the research was the uncovering of elements that relate directly to both students’ class failure and dropout