Records: 322
Academic Communities of Engagement: An expansive lens for examining support structures in blended and online learning
Jered Borup Charles Graham Richard West Leanna Archambault Kristian Spring |
APA Citation
Borup, J., Graham, C. R., West, R. E., Archambault, L., & Spring, K. J. (2020). Academic Communities of Engagement: An expansive lens for examining support structures in blended and online learning. Educational Technology Research and Development, 68(2), 807-832. doi:10.1007/s11423-020-09744-x
Annotation
“Conceptual article proposing the academic communities of engagement (ACE) framework which is focused on how learning support communities can maximize student academic engagement. The ACE framework asserts that students are limited in their ability to independently engage in their online and blended learning but can more fully engage in these activities when scaffolded by others. A student’s ability to engaged and the type of support they require varies depending on the type of engagement. . . . E.g. students with high levels of interest in the topic (affective) but low self-regulation skills (behavioral) or metacognitive (cognitive) would need higher support from instructors in the forms of helping them manage their time and schedule, or better breaking down complex tasks and expectations. Two primary communities support students’ engagement, a personal community and a course community. Personal being the learners support within their local environment who interacts with the student frequently (e.g. parents, friends, social media connections etc). Course community is made up of peers with similar knowledge and professionals who are experiences at supporting students in both learning course content and ways to learn in online and blended environments. Case studies conducted in developing the model showed who supports the student is less important than the background and skills the person has that the student needs (e.g. troubleshooting tech is just as useful from a parent or peer as it is an IT professional if the student walks away with the understanding of how to navigate or troubleshoot the tech). For cognitive engagement, instructor interactions and collaborating with peers prove most impactful. For behavioral engagement, troubleshooting and orienting to tech, organizing and managing, and monitoring progress were the most important types of support needed. Tech orientations were most important for online/blended learning. However in blended learning where students have some control over their pace, hard deadlines help students to better manage their time and maintain progress over the entirety of the course. For affective engagement in blended and online environments, facilitating communication and developing relationships between students is important to keep students engaged in the course. Online ‘netiquette’ and establishing social presence add to this. Conclusions: The ACE framework aims to better understand the types of support students need and various levels to impact their academic success. A clearer understanding of a student’s support communities and the actors within those communities, instructors and institutions can better design build students’ communities to drive their success.
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Adaptive Analytics: It’s about time
Charles Dziuban Colm Howlin Patsy Moskal Tammy Muhs Connie Rachel Johnson Carissa Griffin |
APA Citation
Dziuban, C., Howlin, C., Moskal, P., Muhs, T., & Johnson, C. (2020). Adaptive analytics: It’s about time. Current Issues in Emerging eLearning, 7(1), 42-70.
Annotation
“Quantitative study examining how adaptive learning and learning analytics interact to help students succeed in a college algebra course at two 4-year public institutions, the University of Central Florida as a large metropolitan university, and Colorado Technical University, a primarily online for-profit institutions. At UCF, Realizeit, an adaptive learning platform, was adopted to improve the success rate of students in the college algebra course. Faculty, instructional designers, and support . . . staff broke down course objectives to form each lesson, addressing common student complaints such as textbook readability, course relevance, and textbook cost. At the start of each assignment, students answer a set of questions to determine their current knowledge level, based on those results the platform delivers personalized content and assessments, with an additional option to review alternant content that further explains topics. Personalization included unique references to local UCF events and places, a name bank representative of the student population, and real world application questions personalized to the student’s course of study. The platform also provides learning analytics recommending personalized interventions instructors could review at any time. Modeling development was based on the relationships between the 11 realizeit indices: knowledge state, knowledge covered, calculated, average score, determine knowledge, knowledge state growth, knowledge covered growth, interactions, total time, number revise, and number practice. The multidimensional scaling process interpreted latent clusters resulting in four clusters, knowledge status, growth, engagement, and baseline. The study continued with a two-level procedure to identify which realizeit indices, mediated by student GAP, had the most impact on student success, conducted using regression trees and a logistic regression for dichotomous success. This procedure identified three variables that most effectively predict student success in the course, grad point average, total number of items revised, and total time spend in the course. If students in the lowest GPA quartile could move to quartiles 2-4 in terms of revisions their chance of nonsuccess drops from 74% to 39% and, additionally, if they move to the fourth quartile of time spent in class their nonsuccess rate drops to 4%, suggesting even the students with the lowest GPAs can prove successful using the adaptive platform if they revise a greater number of answers and up their time in the course software.
At CTU, faculty created course content and learning maps based off of course objectives for realizeit with the assistance of the curriculum design team. CTU differed in it’s development efforts int that the top five math faculty members were included in the course development to ensure multiple perspectives. CTU also differed in that all courses were conducted totally online and at an accelerated pace (typically 5.5 weeks), as opposed to the blended and full semester format at UCF. Modeling at CTU was difficult due to the shortened course length, and a model was build by collecting data each week of the course. Variables were: total time, number of activities, nodes attempted, node completed, mean knowledge covered, start day, objectives attempted, objectives completed. The analysis answered the questions, At what point in the course is enough data available to make informed and accurate predictions, and how do the models change from one time slice to the next. For the first two weeks there was no improvement, at week three there is enough improvement to beat the baseline and has enough data to sufficiently signal student success or nonsucces.Overall, both institutions reflect a three week minimum data collection period in order for the learning analytics to be useful to instructors in intervene and enhance student success. At CTU this leaves little time for intervention in comparison to the traditional semester model at UCF. Students with the lowest GPAs can significantly improve their course success rate by revising more answers and spending greater time in the adaptive course platform.
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Adjunct Faculty Participation in the Centralized Design of Online Courses
Sarah Felber |
APA Citation
Felber, S. (2020). Adjunct faculty participation in the centralized design of online courses. Online Journal of Distance Learning Administration, 23(1). https://www.westga.edu/~distance/ojdla/spring231/felber231.html
Annotation
“This article reviews existing literature on adjunct faculty participation in online course design. The research is guided by the research question, “What are the best practices for fostering effective course design and teaching in centrally designed online courses taught by adjunct faculty?” Using Cotton, Vollrath, Froggatt, Lengnick-Hall, and Jennings’ (1988) categories of “participative decision-making” (PDM) as a framework, the research explores studies from the Educational Research . . . Information Center (ERIC) database from 2014 through June 2018 that included search terms, “college faculty,” “participation,” and either “curriculum,” curricular,” or “course.” The researcher examined nine studies, with four studies specifically connected to online courses. The article concludes that information on adjunct faculty input into centralized online courses appears lacking in the literature. The author provides ideas toward including adjunct faculty in the online course design process, while also acknowledging that not all adjunct faculty may desire to be a part of that process.
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An Experiential Online Training Approach for Underrepresented Engineering and Technology Students
Arefeh Mohammadi Kevin Grosskopf John Killingsworth |
APA Citation
Mohammadi, A., Grosskopf, K., & Killingsworth, J. (2020). An experiential online training approach for underrepresented engineering and technology students. Education Sciences, 10(3), 46. doi:10.3390/educsci10030046
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“Quasi-experimental quantitative study examining online training modules impact on learning outcomes in manufacturing college courses with particular focus on woman, older adults, and ethnic minorities at 5 community colleges over 4 years. 342 participants 75 exposed to the online training module and learning outcomes (test scores, course completion and earning the academic certificate) were compared between students who took the online training and those who did not. Students completed 82 . . . modules using Tooling-U and their test scores and course completion metrics were pulled quarterly. Results indicated that African American Participants achieve greater improvement between pre- and post- test scores in the training modules but Hispanics achieved higher module completion rates. Results also indicate that the training modules in conjunction with traditional instruction improved learning outcomes for most participants, particularly older adults, ethnic minorities, and women. Conclusions: the online training modules, used in conjunction with traditional college instructions improved test scores and course completion for students, particularly for older and minority students. African American Students achieved greater improvement on pre-/post- tests within the modules with Hispanic students having higher module completion rates and students achieving 20% or greater improvement between pre- and post- testes were twice as likely to complete credit hours towards the manufacturing certificate or degree.
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Challenges in the online component of blended learning: A systematic review
Rasheed Abubakar Rasheed Amirrudin Kamsin Nor Aniza Abdullah |
APA Citation
Rasheed, R. A., Kamsin, A., & Abdullah, N. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education, 144, 1-17. doi:10.1016/j.compedu.2019.103701
Annotation
“Systematic review of literature 2014-2018 exploring what challenges students, instructors, and institutions face in the online component of blended learning. Five themes emerged in the student challenges: self-regulation (student behaviors that deter them from planning out and achieving their course requirements), technological literacy and competency (student’s proficiency in the use of tech for studying), student isolation (the emotional discomfort and loneliness students suffer when . . . studying outside of f2f classes), technological sufficiency (student access to sufficient online technologies and services), and technological complexity challenges (challenges students face when technologies are overly complex for their studies). Four themes emerged in instructor challenges: technological literacy and competence (instructor’s illiteracy and lack of competency to use tech for teaching), technological operation challenges (challenges with operating technology during the course) , instructor belief challenges (negative perceptions of tech and online learning), and other. Three themes emerged in institutional challenges: Technological provision challenges (providing suitable technology and support services needed for blended learning), instructor training challenges (the lack of ability to effectively train instructors), and other. Self-regulation was the top student challenge with students struggling with procrastination, limited preparation before class, poor time management, and improper utilization of online peer learning strategies. Student’s second top challenge was tech literacy and competency as studies reflected students struggling with handling different user interfaces, lack of tech competency, tech illiteracy, resistance to seek online help, poor understanding of directions and expectations of online work, and student’s perception of technology as a barrier to online help seeking. Researchers have stressed placing students as the center of learning experiences not the technology, as collaborative and interactive online communities offer students a sense of ownership and significantly impact student learning outcomes. The two components of blended learning (online/f2f) support each other in reducing the challenges and worries associated with each component. Instructors commonly reported lacking the experience and technological skill to create engaging online content (particularly online videos). Instructors with less tech literacy were commonly resistant to adding technology to their courses or shifting to online/blended learning, which this review suggests may be remedied by additional instructor training and tech support offered in ways that is not perceived as burdensome. Institutions struggle with determine the level of tech innovation, robustness and complexity needed, while also supporting the cots of the tech, maintenance, and training costs. Conclusions: In general blended learning research has focused more on student challenges. Student self regulation was identified as a problem that must be remedied as it feeds into every other challenge students face. Technological access, literacy, and competence stands as a complex challenge impacting students, instructors, and institutions that must be carefully considered and supported at every level.
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Comparing synchronous and asynchronous online discussions for students with disabilities: The impact of social presence
Ibrahim Dahlstrom-Hakki Zachary Alstad Manju Banerjee |
APA Citation
Dahlstrom-Hakki, I., Alstad, Z., & Banerjee, M. (2020). Comparing synchronous and asynchronous online discussions for students with disabilities: The impact of social presence. Computers & Education, 150, 1-11. doi:10.1016/j.compedu.2020.103842
Annotation
“Mixed methods study conducted in an introductory statistics course at a private 4-year college that exclusively serves students with learning disabilities, ADHD, and ASD, with 4 focus groups and 5 1-1 interviews and a pre-/post-test design. No description of how the course was divided/blended, course design, or instructional approaches beyond discussion types. This study assessed the impact of social presence on the conceptual understanding of course concepts, quantitative analysis was . . . conducted to determine gains in test scores post discussion types (asynchronous or synchronous) and qualitative analysis gathered student perceptions. Students were assigned to a section participating in either solely synchronous online discussions or asynchronous discussions (required to participate over a 24 hour period) throughout the semester to compare gains in test scores. Participants saw a modest gain in scores post asynchronous discussions but no gain in synchronous. Students preferred synchronous discussions over asynchronous for online discussions stating this allowed them to ask the instructor clarifying questions and the ability to interact directly with the instructor and their peers with immediate feedback. Synchronous discussions gave students an increase sense of accountability as the sessions were a specific time each week and non-participation would be immediately evident-saying this helped them stay more organized, motivated, and on task. A minority of participants preferred asynchronous due to the environment being less distracting and less anxiety inducing, with several students expressing difficulty focusing when they had to access a number of live feeds all at once, and the pace of the discussion going too quickly to follow. Technical issues more highly impacted synchronous students, and technical issues would often cause students to disengage entirely from the content. Students expressed preference for traditional f2f discussions as this was closer to traditional classroom discussions and therefore more familiar, and feeling a greater sense of belonging to the class. Conclusion: Students preferred synchronous discussions due to familiarity, but only saw modest gains in test scores after asynchronous discussions, with no improvement following synchronous discussions.
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EDUCAUSE 2020 Student Technology Report: Supporting the Whole Student
Dana Gierdowski Christopher Brooks Joseph Galanek |
APA Citation
Gierdowski, D. C., Brooks, D. C., & Galanek, J. (2020). EDUCAUSE 2020 student technology report: Supporting the whole student. Louisville, CO: EDUCAUSE Research.
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“The 2020 EDUCAUSE Center for Analysis and Research (ECAR) Student Technology Report refocuses from prior years, with an expanded emphasis on the “whole student.” The areas examined in the report include issues such as “online harassment,” that may or may not occur within the formal academic and institutional setting. The ECAR report findings were based on 16,162 student survey responses, from 71 institutions, collected from January 14, 2020 until March 11, 2020, even though the survey was . . . available for a longer period of time. Responses collected after the COVID-19 pandemic was declared were not included. Similar to the 2019 report, student responses showed a preference (58%) for a mix of face-to-face and online elements in a course. However, the report notes that the overall student preference still seems to be face-to-face.The authors raise a concern that this preference exists despite research supporting online course quality, and wonder if the sudden emergency move online due to COVID-19 may further turn student attitudes against online courses in the future.
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Internet-based alternatives for equitable access, preparation, and success in gateway courses
Daniel Hickey Jill Robinson Stefano Fiorini Yanan Feng |
APA Citation
Hickey, D. T., Robinson, J., Fiorini, S., & Feng, Y. (2020). Internet-based alternatives for equitable preparation, access, and success in gateway courses. The Internet and Higher Education, 44, 1-9. doi:10.1016/j.iheduc.2019.100693
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“This study examined which programs led students to successful access to, and completion of, a gateway chemistry course: remedial pre-requisites (e.g. Chem 101/103 ‘prep chem’), ALEKS system, and direct admission. Study conducted at a large, public, 4-year, doctoral granting research university in the midwestern US 15.4% minority, `8.6% pell grant, 13.3% first gen. Study found underrepresented student groups (race/ethnicity, first generation, pell grant) were overrepresented in the remedial . . . pre-req course enrollments. Students were given the option of completing the ALEKS Summer Prep Course for Chemistry, spending an average of 33 hours to achieve the 90% score needed to enroll in the gateway chemistry course. Students who gained access via ALEKS achieved lower grades than those directly admitted to the course, yet achieved significantly higher grades than those students gaining access to the course via the remedial courses. In particular, African American students using the ALEKS system did significantly better than their preparatory course counterparts. Conclusion: Findings suggest that 30 hours of work on the ALEKS system better prepared students, particularly underrepresented students, for the gateway course than the traditional 3 credit (103 only) or 5 credit (101 & 103) preparatory courses.
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Learning Technology Models that Support Personalization within Blended Learning Environments in Higher Education
Hamdan Alamri Sunnie Watson William Watson |
APA Citation
Alamri, H. A., Watson, S., & Watson, W. (2020). Learning technology models that support personalization within blended learning environments in higher education. TechTrends, 65, 62-78. doi:10.1007/s11528-020-00530-3
Annotation
” Systematic review exploring personalized learning in blended learning. Open digital badges, competency based learning technology, and adaptive learning technology were identified as emerging learning tech models that support personalized learning in higher ed. Digital badges are utilized to motivate learners to use online materials and complete online activities often beyond course requirements. Open badges allow students to share badges to social networks and can be verified to increase . . . employment opportunities. Adaptive learning tech allow instructors to organize learning content to be personalized based on student’s abilities and needs through learning resources or personalized profiles and interface, improving student’s mastery of the content. Various institutions have began using McGraw-Hill’s computer-based adaptive platforms that provides unique personalized experiences for learners and helps educators manage and track student progress through learning analytics. 20 case studies have show these programs have increased student retention by 19%, pass rates almost 13% and average test scores by 9%, and have improved instructors time management. Competency-based learning provides flexibility to allow students to build the unique skills they need and want for greater content mastery and employability. Learning analytics are at the heart of personalized learning so institutions need a robust analytics program and institutional resistance to move away from a one size fits all approach can arise as a challenges. Conclusions: Three technologies were identified as supporting personalized learning in blended environments, digital badges, adaptive learning technology, and competency based learning. All three approaches have been implemented at various universities with particularly adaptive technology showing significant impacts on student retention, pass rate and exam scores.
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Online Learning Readiness
Tanya Joosten Rachel Cusatis |
APA Citation
Joosten, T., & Cusatis, R. (2020). Online Learning Readiness. American Journal of Distance Education, 34(3), 180-193. doi:10.1080/08923647.2020.1726167
Annotation
“Quantitative study examining the relationship between student online readiness and student outcomes in online courses conducted two public midwestern universities, one 4-year doctoral granting institution and one 2-year technical school. Data was collected via a student survey of self reported readiness and the institutions’ student information systems including demographic and performance data. Survey had students report on 6 areas of online readiness: online work skills, social technology, . . . familiarity, online learning efficacy, self-directedness, organization, and socialization as well as their learning, performance, and satisfaction. Multiple regression analyses were conducted to examine student preparedness and readiness as a predictor of student outcomes, hierarchical regressions to control for student demographics, with regressions modeling reported learning, satisfaction, and final grades.“MANOVA was used to examine any differences between the vector of means in the four (4) underrepresented groups for the final research question.†(p. 186) allowing for the examination of the underrepresented groups of interest, students with a disability, racial minority, low income students, and first-generation students. Results indicate online learning efficacy and work skills are significantly and positively related to satisfaction and final grades while socialization was significantly and negatively associated with satisfaction. Online efficacy was significantly and positively associated with underrepresented students’ perceptions of learning, reported satisfaction, online work skills, and final grades. Socialization was significantly and negatively associated with their satisfaction. Students with disabilities reported lower perceptions of organization and self directedness while minority students reported lower perceptions of organization, self directedness, and online work skills, but higher preferences for socialization. Conclusions: Online learning efficacy impacted all student outcomes, with online work skills and socialization standing as predictors of learning and satisfaction, meaning minorities and students with disabilities may be at a disadvantage in comparison to their peers. Institutions should seek to better understand and support online learning readiness, particularly in underrepresented student groups.
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