The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Catherine L Finnegan - One of the best experts on this subject based on the ideXlab platform.
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differences by course discipline on student behavior persistence and achievement in Online courses of undergraduate general education
Journal of College Student Retention: Research Theory and Practice, 2008Co-Authors: Catherine L Finnegan, Libby V Morris, Kangjoo LeeAbstract:This research empirically examined student behavior in Online courses and its relationship to persistence and achievement across fields. Eight variables descriptive of student behaviors Online were measured for 1) frequency and 2) duration of Participation. Twenty-two courses were grouped into three broad fields: English and Communication; Social Sciences; and Math, Science, and Technology. The descriptive data revealed significant differences in student Online Participation, persistence, and achievement across the fields. Multiple regression analyses were used to evaluate how well student Participation measures predicted achievement. Depending on the field, different portions of the variability in achievement were accounted for by student Participation measures.
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tracking student behavior persistence and achievement in Online courses
Internet and Higher Education, 2005Co-Authors: Libby V Morris, Catherine L FinneganAbstract:The purpose of this research was to examine student engagement in totally asynchronous Online courses through an empirical analysis of student behavior Online and its relationship to persistence and achievement. A total of 13 sections of three undergraduate, general education courses provided the setting for the study. Three hundred fifty-four students were used in the data analysis. Using student access computer logs, student behaviors defined as frequency of Participation and duration of Participation were documented for eight variables. The descriptive data revealed significant differences in Online Participation between withdrawers and completers and between successful completers and non-successful completers. A multiple regression analysis was used to evaluate how well student Participation measures predicted achievement. Approximately 31% of the variability in achievement was accounted for by student Participation measures, and three of the eight variables were statistically significant.
Libby V Morris - One of the best experts on this subject based on the ideXlab platform.
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differences by course discipline on student behavior persistence and achievement in Online courses of undergraduate general education
Journal of College Student Retention: Research Theory and Practice, 2008Co-Authors: Catherine L Finnegan, Libby V Morris, Kangjoo LeeAbstract:This research empirically examined student behavior in Online courses and its relationship to persistence and achievement across fields. Eight variables descriptive of student behaviors Online were measured for 1) frequency and 2) duration of Participation. Twenty-two courses were grouped into three broad fields: English and Communication; Social Sciences; and Math, Science, and Technology. The descriptive data revealed significant differences in student Online Participation, persistence, and achievement across the fields. Multiple regression analyses were used to evaluate how well student Participation measures predicted achievement. Depending on the field, different portions of the variability in achievement were accounted for by student Participation measures.
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tracking student behavior persistence and achievement in Online courses
Internet and Higher Education, 2005Co-Authors: Libby V Morris, Catherine L FinneganAbstract:The purpose of this research was to examine student engagement in totally asynchronous Online courses through an empirical analysis of student behavior Online and its relationship to persistence and achievement. A total of 13 sections of three undergraduate, general education courses provided the setting for the study. Three hundred fifty-four students were used in the data analysis. Using student access computer logs, student behaviors defined as frequency of Participation and duration of Participation were documented for eight variables. The descriptive data revealed significant differences in Online Participation between withdrawers and completers and between successful completers and non-successful completers. A multiple regression analysis was used to evaluate how well student Participation measures predicted achievement. Approximately 31% of the variability in achievement was accounted for by student Participation measures, and three of the eight variables were statistically significant.
Dean Eckles - One of the best experts on this subject based on the ideXlab platform.
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the behavior chain for Online Participation how successful web services structure persuasion
International Conference on Persuasive Technology, 2007Co-Authors: B J Fogg, Dean EcklesAbstract:The success of many Online services today depends on the company's ability to persuade users to take specific actions, such as registering or inviting friends. We examined over 50 popular Web services of this kind to understand the influence processes and strategies used. We found that successful Online services share a pattern of target behaviors that can be viewed as part of an overall framework. We call this framework the "Behavior Chain for Online Participation." This paper briefly presents the general idea of a behavior chain and applies it to understanding persuasion patterns found Online. We then illustrate the Behavior Chain for Online Participation by applying it to the Web service LinkedIn and other popular services. Future research may identify behavior chains in other domains and develop new research methods for validating behavior chains.
Ashley Shaw - One of the best experts on this subject based on the ideXlab platform.
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scholars in an increasingly open and digital world imagined audiences and their impact on scholars Online Participation
Learning Media and Technology, 2018Co-Authors: George Veletsianos, Ashley ShawAbstract:ABSTRACTThis study investigates the audiences that scholars imagine encountering Online and the ways in which these audiences impact scholars’ Online Participation and presentation of self. Prior research suggests that imagined audiences affect what users share and how they present themselves on social media, but little research has examined this topic in the context of faculty members and doctoral students (i.e., scholars). An analysis of interviews with 16 scholars shows that imagined audiences span the personal–professional continuum. Further, most scholars imagined their Online audiences as known and familiar. Though many recognized collapsed contexts as problematic, several others appeared more comfortable with audience collapse than prior literature suggests. Findings also suggest that scholars’ conceptualizations of their audiences differ from those of their universities, principally in that scholars imagine their audiences as communities rather than as venues for attracting professional attention.
Evangelos Pournaras - One of the best experts on this subject based on the ideXlab platform.
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Decentralized Collective Learning for Self-managed Sharing Economies
ACM Transactions on Autonomous and Adaptive Systems, 2018Co-Authors: Evangelos Pournaras, Peter Pilgerstorfer, Thomas AsikisAbstract:The Internet of Things equips citizens with a phenomenal new means for Online Participation in sharing economies. When agents self-determine options from which they choose, for instance, their resource consumption and production, while these choices have a collective systemwide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This article envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy, and Participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning. This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections, accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that involve non-local brute-force operations or exchange full information. This article contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic tradeoffs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies.
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Self-Adaptive Learning in Decentralized Combinatorial Optimization - A Design Paradigm for Sharing Economies
2017 IEEE ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 2017Co-Authors: Peter Pilgerstorfer, Evangelos PournarasAbstract:The democratization of Internet of Things and ubiquitous computing equips citizens with phenomenal new ways for Online Participation and decision-making in application domains of smart grids and smart cities. When agents autonomously self-determine the options from which they make choices, while these choices collectively have an overall system-wide impact, an optimal decision-making turns into a combinatorial optimization problem known to be NP-hard. This paper contributes a new generic self-adaptive learning algorithm for a fully decentralized combinatorial optimization: I-EPOS, the Iterative Economic Planning and Optimized Selections. In contrast to related algorithms that simply parallelize computations or big data and deep learning systems that often require personal data and overtake of control with implication on privacy-preservation and autonomy, I-EPOS relies on coordinated local decision-making via structured interactions over tree topologies that involve the exchange of entirely local and aggregated information. Strikingly, the cost-effectiveness of I-EPOS in regards to performance vs. computational and communication cost highly outperforms other related algorithms that involve non-local brute-force operations or exchange of full information. The algorithm is also evaluated using real-world data from two state-of-the-art pilot projects of participatory sharing economies: (i) energy management and (ii) bicycle sharing. The contribution of an I-EPOS open source software suite implemented as a paradigmatic artifact for community aspires to settle a knowledge exchange for the design of new algorithms and application scenarios of sharing economies towards highly participatory and sustainable digital societies.
