Browsing by Subject "Online Learning"

Now showing 1 - 4 of 4
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    Meeting Students in the Margins: Exploring the Use of Social Annotation by Undergraduate Online Instructors
    (2023-11) Avadhanam, Rukmini Manasa
    Social Annotation (SA) is a learning technology that allows people to read, highlight, and comment on specific parts of text. SA tools like Hypothes.is enable users to highlight and annotate texts and documents online and respond to others’ annotations via text, sharing links of documents, audio, or video. Research on SA in higher education online learning has increased exponentially in the past two decades. However, this rich body of literature mainly studied the evaluation of SA tools and their effectiveness on student-related measures. However, very few studies discuss instructors’ perspectives and their use of social annotation. There needs to be more knowledge about the processes and challenges instructors face in using and implementing social annotation in undergraduate online courses. The lack of studies on instructor perspectives on social annotation makes it challenging to understand the teaching, assessment, and participation strategies that effectively achieve the course objectives, improve student learning outcomes, and engage students in learning. This study aims to understand how and why instructors use social annotation to achieve their pedagogical goals, the processes behind the thoughtful and intentional design of social annotation activities for their online classes, and their perception of how it impacts student learning experiences. This qualitative, descriptive case study delves into instructors’ design and pedagogical processes using social annotation tools for their online undergraduate courses. The findings of this study illustrate rich descriptions of instructor design and implementation processes of five instructors teaching online courses in two modalities, asynchronous and synchronous. It details how the course objectives, context, design, and pedagogical processes influence learner participation in various social annotation activities. Thematic analysis of qualitative data sources also elaborates that instructors use social annotation tools to create an authentic, collaborative learning community for student discussion and to ensure student perspectives are more visible. Instructors’ design and pedagogical processes, like providing guiding prompts, participation-based assessment strategies, and instructor participation to further student discussion, are also evident. The study’s implications indicate how there should be more focus on instructor use of learning technologies, support them institutionally with professional development, and communities of practice.
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    Personalized Online Self-Learning
    (2021-05) Pandey, Shalini
    With the growth of the internet, online learning platforms such as edX, Coursera, and Udacity have emerged. The Massive Open Online Courses (MOOCs) provided by these learning platforms are changing the landscape of education. The advantage of MOOCs is that they make courses available at a nominal price to students all across the globe. With the ability to reach a large number of learners around the world, MOOCs have made a positive impact on education. In addition, professional learners take these courses to achieve professional and career growth. This increases the audience size of the learning platform. Recent studies have shown that MOOCs have emerged as a disruptive technology with the potential of changing the shape of the existing educational setting. Despite the convenient settings provided by MOOCs, dropout rates on the learning platforms remain elevated. Some learners who drop out report a lack of support by these platforms as a major reason for their disengagement. A factor contributing to this lack of personal guidance is that the online learning platforms follow one-size-fits-all and are not customized for different individuals. Currently, in most of the online education settings student has to determine everything, from what courses to pick to what questions to solve. Instead, an ideal learning system must scaffold the learning process—from initial modeling and coaching-oriented feedback to a gradual release of responsibility to students. Without sustained student input and feedback, their talents, creativity, and efficacy can be overlooked or negated. To tackle this problem, we need to develop systems that support self-learning. Personalized self-learning is defined as a teaching and learning process that assists learners based on the strengths, needs, and interests of individual learners while enhancing the self-learning experience. Massive data generated by online learning platforms have made research in this direction possible. Machine learning and data mining communities are focusing on the application of AI in MOOC education research. The first step leading to the development of personalized systems is to identify the needs of individual learners. In an online education system, we must determine the strengths and weaknesses of learners before customizing the platform to their condition. A system to assess learner's knowledge can also help in proving a justification to learners regarding what they need to focus on or what learning trajectory to follow. Second, we personalize the recommendation of forums to improve the experience of students on MOOCs. The discussion forums have become an open-source venue for sharing the knowledge which generates an auxiliary source of learning. For students taking the online courses, this auxiliary material can help the interested student have a constructive discussion with their peers. However, it is difficult for them to browse through the enormous amount of forums to find the relevant thread of their interest. Lastly, we aid self-learners who join the online learning system for developing a specific skill (such as machine learning) or learning a particular concept. Specifically, we provide them the pre-requisite concepts to master before focussing on their goal concept. We believe that this information can help the learners pick the concepts and videos to watch more intelligently. In addition, we also recommend the next videos for learners to watch based on their interaction behavior in the past. For this, we develop a novel representation learning technique that leverages rich information about their textual content and structural relations between entities. In summary, this thesis contributes towards the development of personalized MOOC platforms, specifically providing the following application 1)Knowledge Assessment to determine the strengths and weaknesses of students, 2) Forum recommendation to recommend relevant forums to students, 3) Concept Pre-requisite Prediction to predict pre-requisite relations between different knowledge concepts, and 4) Learning Path recommendation to recommend the sequence of videos a student needs to pick to achieve their goals.
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    Preferences, Pedagogical Strategies, and Challenges of Instructors Teaching in Multiple Delivery Formats within A 2-Year College Context
    (2015-05) Huang, Li-chin
    This case study examines general education instructors' preferences, pedagogical strategies, and challenges in delivering face-to-face (f2f), hybrid, and online multiple delivery formats (MDF) at a 2-year technical college. Its purpose is three-fold: to produce a detailed description of instructors' MDF experiences, provide recommendations for improving MDF teaching, and better inform relevant stakeholders about the cultural contexts and practices that affect MDF implementation. In this case study, four selected faculty members participated in a two-hour face-to-face interview with the researcher following a semi-structured, open-ended questionnaire. They also completed the Conti teaching style inventory to examine their pedagogical adjustments and for data triangulation purposes. A follow-up collection of relevant data in the form of syllabi, learning plans, and assessments was conducted after the interviews were transcribed. The researcher also collected data from documents generated within college and departmental meetings and from informal conversations with her colleagues regarding MDF issues and experiences. Six main themes emerged from this research: (a) learner characteristics were the major pedagogical concern of participants across all different delivery formats; (b) the f2f mode was the most effective and favorite format, and the hybrid mode was the least; (c) the hybrid format was time-consuming and entailed a clear teaching-learning framework; (d) learners' personal life circumstances involved in learning; (e) MDF faculty needed to be competent at integrating technology, pedagogy, and content knowledge; and (f) faculty members' time was spread too thin over multiple MDF delivery preps. The two most recurring themes in the individual cases were that the participants' experiences were determined primarily by learners' characteristics and that they worked within in an unclear hybrid framework. The suggestions for improving MDF practices were provided.
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    Understanding Students’ Self-Regulation in Asynchronous Online Learning
    (2019-05) North, Sarah
    Despite rapid growth in online enrollment within higher education, persistence and completion rates remain lower for online courses than face-to-face courses. This discrepancy between the two modalities indicates a need to better understand students’ self-regulated learning (SRL) within online learning environments. Students with higher SRL skills demonstrate higher academic achievement than those who do not, and so it is critical to investigate the topic of SRL because it is so closely tied with achievement online. This study used a sequential, explanatory mixed methods approach to better understand the experience and actions of undergraduate students in an asynchronous online course who possess varying levels of self-regulation. In the quantitative phase, participants completed the Motivated Strategies for Learning Questionnaire which gave a self-reported snapshot into students’ motivation, self-regulating skills, and learning strategies. Trace log data from the learning management system (LMS) was additionally collected during four weeks of the semester. During an interim phase, three focal participants were selected and a semi-structured interview protocol was developed for the qualitative phase. The qualitative phase consisted of data collected through interviews with each of the focal participants, and observations of the three participants throughout the semester. Results suggest that while students appreciate the flexibility of an online course, flexibility can also lead to challenges. The flexible nature of a course appeared most challenging during online group work, when taking an online class for the first time, or when time management was poor. It was also found that students with higher levels of SRL strategies tend to dedicate specific time and places to work on coursework, and demonstrated a propensity to log in to the course LMS earlier and more frequently during each course week. Conversely, it was found that a student with lower SRL abilities did not dedicate a specific time or place to studying for the course, and tended to miss group discussion deadlines. Finally, it was found that an online instructors’ presence, frequent communication, use of video posts and discussions, and outlining weekly expectations were helpful teaching strategies which encouraged students to maintain motivation and SRL within the course.