{"title":"Blended Course Design: A Synthesis of Best Practices","authors":"P. McGee, A. Reis","doi":"10.24059/OLJ.V16I4.239","DOIUrl":"https://doi.org/10.24059/OLJ.V16I4.239","url":null,"abstract":"Blended or hybrid course offerings in higher education are commonplace and much has been written about how to design a blended course effectively. This study examines publicly available guides, documents, and books that espouse best or effective practices in blended course design to determine commonalities among such practices. A qualitative meta-analysis reveals common principles regarding blended definitions, the design process, pedagogical strategies, classroom and online technology utilization, assessment strategies, and course implementation and student readiness. Findings reveal areas of disconnect and conflict, as well as implications for the likelihood of successful utilization when best/effective practices are followed.","PeriodicalId":298605,"journal":{"name":"Journal of asynchronous learning networks","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122228279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Interaction in an asynchronous online course : a synthesis of quantitative predictors","authors":"Daniel Zingaro, Murat Oztok","doi":"10.24059/OLJ.V16I4.265","DOIUrl":"https://doi.org/10.24059/OLJ.V16I4.265","url":null,"abstract":"The effectiveness and potential of asynchronous online courses hinge on sustained, purposeful collaboration. And while many factors affecting interaction have been uncovered by prior literature, there are few accounts of the relative importance of these factors when studied in the same online course. In this paper, we develop a literature-informed model of six predictors on the likelihood that a note receives a reply. We corroborate earlier findings (such as the impact of the date that the note was posted) but also obtain one contradictory result (that reading ease does not appear to be a significant predictor). We offer hypotheses for our findings, suggest future directions for this type of research, and offer educational implications.","PeriodicalId":298605,"journal":{"name":"Journal of asynchronous learning networks","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122336652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using a Generalized Checklist to Improve Student Assignment Submission Times in an Online Course.","authors":"Terence Cavanaugh, Marcia Lamkin, Haihong Hu","doi":"10.24059/OLJ.V16I4.235","DOIUrl":"https://doi.org/10.24059/OLJ.V16I4.235","url":null,"abstract":"Learning environments such as web-based instruction require more learner self-control and proactive learning to construct knowledge and acquire skills. However, online students often fail to complete some components of their online work each week, damaging their overall academic progress in the course. To assist students in completion and submission of work, three professors at a public southeastern university implemented the use of a generalized assignments checklist to enhance student self-monitoring in their online courses. Data on the submission of assignments was analyzed for relative timeliness. The results of this study showed a statistically significant difference based on the checklist received students to the control group, with a marked improvement of assignment submission timeliness, improving course satisfaction for students and instructors.","PeriodicalId":298605,"journal":{"name":"Journal of asynchronous learning networks","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124959717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Do Students Experience \"Social Intelligence,\" Laughter, and Other Emotions Online?.","authors":"K. Meyer, Stephanie J. Jones","doi":"10.24059/OLJ.V16I4.283","DOIUrl":"https://doi.org/10.24059/OLJ.V16I4.283","url":null,"abstract":"Are online activities devoid of emotion and social intelligence? Graduate students in online and blended programs at Texas Tech University and the University of Memphis were surveyed about how often they laughed, felt other emotions, and expressed social intelligence. Laughter, chuckling, and smiling occurred “sometimes,” as did other emotions (e.g., anticipation, interest, surprise). The capacities comprising social intelligence were also experienced “sometimes,” but more frequently in online classes than in non-class-related online activities. The students were mostly likely to present themselves effectively and care about others and least likely to sense others’ emotions. In a comparison of social intelligence capacities in the online course and other non-course-related but online activities (e.g., surfing and gaming), a paired t-test confirmed that the means were different (p < 0.05) and perhaps documented greater occurrence of social intelligence in the online course setting.","PeriodicalId":298605,"journal":{"name":"Journal of asynchronous learning networks","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121340318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Comparison of Non-Mandatory Online Dialogic Behavior in Two Higher Education Blended Environments","authors":"P. Gorsky, A. Caspi, I. Blau","doi":"10.24059/OLJ.V16I4.268","DOIUrl":"https://doi.org/10.24059/OLJ.V16I4.268","url":null,"abstract":"This study compares dialogic behavior in asynchronous course forums with non-mandatory student participation at a campus-based college and at a distance education, Open University. The goal is to document similarities and differences in students' and instructors' dialogic behavior that occur in two similar instructional resources used in two dissimilar learning environments. Quantitative content analysis, derived from the \"Community of Inquiry\" model, was performed on a year-long course forum from the college. These data were compared with data obtained previously from the Open University course forums. Findings showed that the dialogic behavior in the college forum differed greatly from the dialogic behavior exhibited in distance education forums. Specifically, the frequencies of \"social presence\", \"teaching presence\" and \"cognitive presence\" in the forums differed significantly. However, high frequencies of social presence coupled with low frequencies of cognitive presence at both institutions raise doubts regarding the popular assumption that deep and meaningful learning occurs in asynchronous course forums.","PeriodicalId":298605,"journal":{"name":"Journal of asynchronous learning networks","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123336094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Learning Analytics Considered Harmful.","authors":"L. Dringus","doi":"10.24059/OLJ.V16I3.272","DOIUrl":"https://doi.org/10.24059/OLJ.V16I3.272","url":null,"abstract":"This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially \"harmful\" application of learning analytics.","PeriodicalId":298605,"journal":{"name":"Journal of asynchronous learning networks","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115654592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Ice, S. Diaz, Karen Swan, Melissa Burgess, Mike Sharkey, Jonathan Sherrill, Daniel Huston, H. Okimoto
{"title":"The PAR Framework Proof of Concept: Initial Findings from a Multi-Institutional Analysis of Federated Postsecondary Data","authors":"P. Ice, S. Diaz, Karen Swan, Melissa Burgess, Mike Sharkey, Jonathan Sherrill, Daniel Huston, H. Okimoto","doi":"10.24059/OLJ.V16I3.277","DOIUrl":"https://doi.org/10.24059/OLJ.V16I3.277","url":null,"abstract":"Despite high enrollment numbers, postsecondary completion rates have generally remained unchanged for the past 30 years and half of these students do not attain a degree within six years of initial enrollment. Although online learning has provided students with a convenient alternative to face-to-face instruction, there remain significant questions regarding online learning program quality, particularly when considering patterns of student retention and progression. By aggregating student and course data into one dataset, six postsecondary institutions worked together toward determining factors that contribute to retention, progression, and completion of online learners with specific purposes: (1) to reach consensus on a common set of variables among the six institutions that inform student retention, progression and completion; (2) to explore advantages and/or disadvantages of particular statistical and methodological approaches to assessing factors related to retention, progression and completion. In the relatively short timeframe of the study, 33 convenience variables informing retention, progression, and completion were identified and defined by the six participating institutions. This initiative, named the Predictive Analytics Reporting Framework (PAR) and the initial statistical analyses utilized are described in this paper.","PeriodicalId":298605,"journal":{"name":"Journal of asynchronous learning networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126405490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Evolution of Big Data and Learning Analytics in American Higher Education","authors":"Anthony G. Picciano","doi":"10.24059/OLJ.V16I3.267","DOIUrl":"https://doi.org/10.24059/OLJ.V16I3.267","url":null,"abstract":"Data-driven decision making, popularized in the 1980s and 1990s, is evolving into a vastly more sophisticated concept known as big data that relies on software approaches generally referred to as analytics. Big data and analytics for instructional applications are in their infancy and will take a few years to mature, although their presence is already being felt and should not be ignored. While big data and analytics are not panaceas for addressing all of the issues and decisions faced by higher education administrators, they can become part of the solutions integrated into administrative and instructional functions. The purpose of this article is to examine the evolving world of big data and analytics in American higher education. Specifically, it will look at the nature of these concepts, provide basic definitions, consider possible applications, and last but not least, identify concerns about their implementation and growth. (Contains 1 table and 2 figures.)","PeriodicalId":298605,"journal":{"name":"Journal of asynchronous learning networks","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132679015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses","authors":"V. C. Smith, Adam Lange, Daniel Huston","doi":"10.24059/OLJ.V16I3.275","DOIUrl":"https://doi.org/10.24059/OLJ.V16I3.275","url":null,"abstract":"Community colleges continue to experience tremendous growth in online courses. This growth reflects the need to increase the numbers of students who complete certificates or degrees. Retaining online students, not to mention assuring their success, is a challenge that must be addressed through practical institutional responses. By leveraging the huge volumes of existing student information, higher education institutions can build statistical models, or learning analytics, to forecast student outcomes. This is a case study from a community college utilizing learning analytics and the development of predictive models to identify at-risk students based on dozens of key variables.","PeriodicalId":298605,"journal":{"name":"Journal of asynchronous learning networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130857546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Learning Analytics: A Case Study of the Process of Design of Visualizations","authors":"Martin Olmos, L. Corrin","doi":"10.24059/OLJ.V16I3.273","DOIUrl":"https://doi.org/10.24059/OLJ.V16I3.273","url":null,"abstract":"The ability to visualize student engagement and experience data provides valuable opportunities for learning support and curriculum design. With the rise of the use of learning analytics to provide “actionable intelligence” [1] on students’ learning, the challenge is to create visualisations of the data which are clear and useful to the intended audience. This process of finding the best way to visually represent data is often iterative, with many different designs being trialled before the final design is settled upon. This paper presents a case study of the process of refining a visualization of students’ learning experience data. In this case the aim was to create a visual representation of the continuity of care students were exposed to during a longitudinal placement as part of a medical degree. The process of visualization refinement is outlined as well as the lessons learnt along the way.","PeriodicalId":298605,"journal":{"name":"Journal of asynchronous learning networks","volume":"31 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130419512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}