Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses

Smith, Vernon C.; Lange, Adam; Huston, Daniel

doi:10.24059/olj.v16i3.275

Cited by 69 publications

(68 citation statements)

References 5 publications

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“…Student use of LCMS-based course content and visits to the online grade book were also positively correlated with final course grade (Dawson, McWilliam & Tan, 2008). Smith, Lange, and Huston (2012) found that LCMS log-in frequency, site engagement (activities such as viewing the course syllabus, viewing an assessment, and completing an assessment), and points submitted were all correlated with successful course outcomes. Also, the more a student performs a certain course activity the better they will score in that area as course outcomes are predicted with increased accuracy as more activity and grade information accumulate over the duration of a course (Smith, Lange, & Huston, 2012).…”

Section: Accepted Manuscriptmentioning

confidence: 93%

Predicting course outcomes with digital textbook usage data

Junco

Clem

2015

The Internet and Higher Education

105

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Section: Accepted Manuscriptmentioning

confidence: 93%

Predicting course outcomes with digital textbook usage data

Junco

Clem

2015

The Internet and Higher Education

105

View full text Add to dashboard Cite

“…Although online courses can be designed on the same pedagogical principles as face-to-face courses, they use different approaches to communicating content and conveying interaction between instructors and students, as well as between students and their peers (Smith, Ferguson, & Caris, 2001;Zhu, Payette, & DeZure, 2003).…”

Section: Interactions In Distance Educationmentioning

confidence: 99%

Learning at distance: Effects of interaction traces on academic achievement

Joksimovíc

Gašević

Loughin

et al. 2015

Computers & Education

138

103

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a b s t r a c tContemporary literature on online and distance education almost unequivocally argues for the importance of interactions in online learning settings. Nevertheless, the relationship between different types of interactions and learning outcomes is rather complex. Analyzing 204 offerings of 29 courses, over the period of six years, this study aimed at expanding the current understanding of the nature of this relationship. Specifically, with the use of trace data about interactions and utilizing the multilevel linear mixed modeling techniques, the study examined whether frequency and duration of studentestudent, studenteinstructor, studentesystem, and studentecontent interactions had an effect of learning outcomes, measured as final course grades. The findings show that the time spent on studentesystem interactions had a consistent and positive effect on the learning outcome, while the quantity of studentecontent interactions was negatively associated with the final course grades. The study also showed the importance of the educational level and the context of individual courses for the interaction types supported. Our findings further confirmed the potential of the use of trace data and learning analytics for studying learning and teaching in online settings. However, further research should account for various qualitative aspects of the interactions used while learning, different pedagogical/media features, as well as for the course design and delivery conditions in order to better explain the association between interaction types and the learning achievement. Finally, the results might imply the need for the development of the institutional and program-level strategies for learning and teaching that would promote effective pedagogical approaches to designing and guiding interactions in online and distance learning settings.

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“…The concept of student engagement in educational context can also be seen as a real predictor of course success. In their research, [11] found a connection concerning student activity in e-learning environment and course outcome. For that reason, specific analysis of student engagement is essential in order to more accurately predict and increase students' success in online courses.…”

Section: Introductionmentioning

confidence: 95%

Data Analysis and Learning Analytics for Measure Effects of Gamification in a Math Online Project

Soares¹,

Babo²,

Azevedo³

et al. 2017

INTED Proceedings

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Nowadays the use of the information and communication technologies (ICT) is more and more common in the learning and teaching process. Modern forms of education have risen and these require the implementation of new learning paradigms: situated cognition, student-centred learning, distributed cognition, constructivism, and communities of practice, among others. However there is an important problem that concerns educators, the lack of student motivation and engagement in education, especially in the e-learning environment (Online Education, Online Courses), where the motivation and the active role of students are definitely the key. Therefore, the interest of researchers in the subject of gamification, which main pillars are motivation, progressiveness and instant feedback, has increased. Furthermore, influence of gamification, depending on utilized components (challenge, curiosity, competion, recognition, etc) may have different effects on students. Thus, there is a need of deeper understanding of dependencies between engagement and implemented gamification elements. The goal of such analysis could be personalization of an e-learning system based on a model that enables management of gamification process individually for every student.The purpose of this paper is to investigate how gamification affects a Mathematics Online Course that is using this model as a pedagogical strategy. It relates such strategy with active learning practices and discusses its effectiveness, investigating how gamification can motivate students to participate more deeply and even to change their self-concept as learners. Moreover, the learning analytics and data analysis shows that implementation of the gamification components contributes to the engagement of students in an e-learning environment, having a positive impact on the final grade.

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Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses

Cited by 69 publications

References 5 publications

Predicting course outcomes with digital textbook usage data

Predicting course outcomes with digital textbook usage data

Learning at distance: Effects of interaction traces on academic achievement

Data Analysis and Learning Analytics for Measure Effects of Gamification in a Math Online Project

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