Time-varying Learning and Content Analytics via Sparse Factor Analysis

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, lecture videos, etc, or the forgetting effect), and (iii) estimates the content organization and intrinsic difficulty of the assessment questions. These quantities are estimated solely from binary-valued (correct/incorrect) graded learner response data and a summary of the specific actions each learner performs (e.g., answering a question or studying a learning resource) at each time instance. Experimental results on two online course datasets demonstrate that SPARFA-Trace is capable of tracing each learner's concept knowledge evolution over time, as well as analyzing the quality and content organization of learning resources, the question-concept associations, and the question intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable or better performance in predicting unobserved learner responses than existing collaborative filtering and knowledge tracing approaches for personalized education

DoR Communicator - October 2014

The October 2014 issue of the Division of Research newsletter.https://digitalcommons.fiu.edu/research_newsletter/1017/thumbnail.jp

Ambient Intelligence with Wireless Grid Enabled Applications: A Case Study of the Launch and First Use Experience of WeJay Social Radio in Education

Wireless grid and ambient intelligent (AmI) environments are characterized as supportive of collaboration, interaction, and sharing. The conceptual framework advanced for this study incorporated the constructs of innovation, creativity and context awareness while offering emergence theory -- emergent properties, structures, patterns and behaviors -- to frame and investigate a wireless grid enabled social radio application which was theorized to be potentially transformative and disruptive. The unintended consequences and unexpected possibilities of wireless grid and smart environments were also addressed. Using a single case study, drawing upon multiple data collection methods, this research investigated the deployment and use experience of WeJay, an application incubated through the Wireless Grids Innovation Testbed (WiGiT), from the perspective of beta trial participants. Guided by the broad research question -- Do wireless grid enabled applications, such as WeJay social radio, add to the potential for new and transformative outcomes for people, information and technology when deployed in an academic setting? -- this empirical study sought to: a) learn more about the launch experience of this first pre-standards wireless grid enabled application among WiGiT members and selected Syracuse University students and faculty; b) understand how this application was interpreted for use; c) determine whether novel and unexpected uses emerged; d) investigate whether wireless grid enabled environments fostered innovation and creativity; and e) elicit whether a conceptual relationship was emerging between wireless grid and AmI environments, focusing on context-awareness and ambient learning. While this early stage of diffusion and first user sample was a key limitation of the study it was also the core strength. Although challenged by the state of readiness of WeJay, study findings supported the propositions that WeJay fosters innovation and creativity; that novel and unexpected uses were generated; and that the theorized relationship between wireless grid applications and embedded awareness does exist. Recommendations for enhanced tool readiness were made and embedded smartness was found to be both desirable and beneficial. This research makes a contribution as a bridge study for future research while having theoretical and methodological implications for research and practice. Social, emotion/affect, and human-centered computing (HCC) dimensions emerged as rich areas for further research

Characterizing Productive Perseverance Using Sensor-Free Detectors of Student Knowledge, Behavior, and Affect

Failure is a necessary step in the process of learning. For this reason, there has been a myriad of research dedicated to the study of student perseverance in the presence of failure, leading to several commonly-cited theories and frameworks to characterize productive and unproductive representations of the construct of persistence. While researchers are in agreement that it is important for students to persist when struggling to learn new material, there can be both positive and negative aspects of persistence. What is it, then, that separates productive from unproductive persistence? The purpose of this work is to address this question through the development, extension, and study of data-driven models of student affect, behavior, and knowledge. The increased adoption of computer-based learning platforms in real classrooms has led to unique opportunities to study student learning at both fine levels of granularity and longitudinally at scale. Prior work has leveraged machine learning methods, existing learning theory, and previous education research to explore various aspects of student learning. These include the development of sensor-free detectors that utilize only the student interaction data collected through such learning platforms. Building off of the considerable amount of prior research, this work employs state-of-the-art machine learning methods in conjunction with the large scale granular data collected by computer-based learning platforms in alignment with three goals. First, this work focuses on the development of student models that study learning through the use of advancements in student modeling and deep learning methodologies. Second, this dissertation explores the development of tools that incorporate such models to support teachers in taking action in real classrooms to promote productive approaches to learning. Finally, this work aims to complete the loop in utilizing these detector models to better understand the underlying constructs that are being measured through their application and their connection to productive perseverance and commonly-observed learning outcomes

Linking Differential Equations to Social Justice and Environmental Concerns

Special issue of the CODEE Journal in honor of its founder, Professor Robert Borrelli

Linking Differential Equations to Social Justice and Environmental Concerns

Special issue of the CODEE Journal in honor of its founder, Professor Robert Borrelli

Antecedents of online community commitment and its effect on behavioural intentions in China

Social media has captured a major part of people’s daily communication in recent years. As an important component of social media platform, online communities has attracted remarkable popularity and has greatly influenced the normal people’s lives. Both researchers and practitioners have predicted this phenomenon will bring new opportunities and challenges to the business. However, due to the new social media phenomenon, research in this field is yet to mature. In addition, because of online community’s virtual features and low switching cost, members frequently join and leave the communities. Therefore, previous researches have highlighted factors that influence customers’ online community engagement and commitment from different perspective. After reviewing the existing literature related to the antecedents of online community commitment, this study aims to study what are the core factors that may influence online community members’ commitment and how it shapes their behavioural intentions. To facilitate this research objective, this study proposes a holistic model with five hypotheses to explore antecedents of online community commitment from three aspects: personal influences, social influences, and online community characteristics based on Relationship Marketing Theory, Social Influence Model, and Framework of Community Characteristics. In addition, this study tests the effects of online community commitment on members’ behavioural intentions. Lastly, this study stresses on adopting advertising as a moderating factor to investigate the moderating role of advertising on the relationship of online community commitment and behavioural intentions. To initiate this research, an online survey approach was taken. A total of 999 validated questionnaires were collected from ten top maternal and baby care related online communities in China, which were selected based on the ranking from http://top.chinaz.com. Partial least squares based structural equation modelling (PLS-SEM) was used to analyse the collected data. Overall, the results indicated that the proposed holistic model of online community commitment fulfil the principles of a parsimonious model with good predictive ability. The results also show that the collected data fits the proposed model well and support all the proposed hypotheses except H5. Specifically, the results revealed that personal influences, social influences, and online community characteristics gave positive effects on online community commitment. In addition, online community commitment also positively influences the members’ behavioural intentions. However, the impact of online community commitment on behavioural intentions moderated by the level of advertising was not supported. Overall, this study proposed and tested a holistic model of online community commitment which has a theoretical significance and also enhance understanding of consumer behaviour in online communities in China, especially in maternal and baby care related industry

Proceedings, MSVSCC 2011

Proceedings of the 5th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 14, 2011 at VMASC in Suffolk, Virginia. 186 pp

When Michigan Changed the World

http://deepblue.lib.umich.edu/bitstream/2027.42/168165/1/2020-Feb_When_UM_Changed_the_World.pd