As faculty we probably have strong feelings about online teaching and we certainly have pet peeves about our students. What if we were to ask our students what their pet peeves about us or our online classes are? (The student assessment of instruction at my own institution doesn’t include a question this direct). And yikes! once we ask, what do we do with that information? Whether you are new to online or an experienced online instruction, come join the conversation and together we will discuss ways to ‘tame the online beast.’

As part of a nationwide effort to increase the postsecondary educational attainment levels of citizens, community colleges have expanded offerings of courses and programs to more effectively meet the needs of students. Online courses offer convenience and flexibility that traditional face-to-face classes do not. These features appeal to students with family and work responsibilities that typically make attending classes on campus difficult. However, many of the students who tend to take courses in this instructional format have characteristics that place them at high-risk for academic failure. Because of the traditional mission of community colleges, they generally serve more students who fit this highrisk profile. Despite the promise and potential of online delivery systems, studies have associated distance education with higher student withdrawal rates. In addition, research has indicated that online students tend to earn lower grades than students in comparable face-to-face classes. The existence of contrasting findings in the literature exposes the need for additional empirical research relative to the overall success of students in online courses, as well as on factors associated with success in distance education. This is especially true for community college students. The purpose of this study was to determine if significant differences existed in student success at the community college level in online courses as compared to face-to-face courses. In addition, the researchers investigated the relationship between selected demographic, academic, enrollment, and external environmental factors and student success in online courses. The study involved secondary data analysis of quantitative data relevant to students enrolled in course sections taught by instructors who taught both online and face-to-face sections of the same course within the same semester from fall 2012 through spring 2015. The target population included 4,604 students enrolled at a public 2-year community college located in Tennessee. Results indicated there was a significant difference in success between students taking a course online and students taking a course face-to-face. Also, there was a significant difference in success based on instructional method when the following factors were considered: age group, gender, student academic classification, and Pell Grant eligibility status. There was no significant difference in success based on instructional method when first-generation college student status was considered.

Although a relatively small number of K-12 students are currently enrolled in online classes, the dramatic growth in online enrollments in recent years suggests that online education will play a significant role in the future landscape of public education. While our understanding of online teaching and learning continues to grow, relatively little is known about the experiences of teachers as they engage in online teaching. In particular, very little is known about the concerns of teachers as they navigate their teaching roles and responsibilities in an online teaching environment. Using an interpretative phenomenological analysis approach, this qualitative study explored the concerns of seven online K-12 teachers through video interviews and bi-monthly journal entries. The findings in this study resulted in six themes with associated sub-themes. These themes suggest that online teachers are highly concerned about themselves, their roles, and their students, along with concerns found at the intersections of these areas. Additionally, this study reveals that the political, educational, and organizational contexts surrounding these online learning environments significantly influence the development and degree of teachers' concerns. The implications of this research encourages greater dialogue between teachers and online educational leaders to better understand teacher concerns and mitigate the negative impact of these concerns on online teachers.

Students' motivation has been identified as a critical factor for meaningful engagement and positive academic achievement in various educational settings. In particular, self-regulation strategies have been identified as important skills in online learning environments. However, applying self-regulation strategies, such as goal setting, strategic planning, and reflect performance takes significant effort. Without motivation, students will not enact these types of strategies. Autonomous self-regulation has been investigated in traditional classroom settings and there is ample empirical evidence of a significant relationship between autonomous self-regulation and engagement and academic achievement. However, such research was limited in online learning environments. The purpose of this study was to investigate the factors that affected students' autonomous or self-determined forms of regulation as defined in self-determination theory (SDT). The study examined the relations between students' self-regulated motivation and four other variables (students' interests in the course, students' perception of their instructor's interaction type, students' technology self-efficacy, and students' perception of the degree to which their online learning environment used constructivist-based pedagogy), and the interactions among these variables in college online courses. In addition, the study examined the relationship between students' autonomous forms of regulation and their engagement, learning achievement, interaction behaviors, and satisfaction in the online course. For students' interaction behaviors, the total number of authored and read messages, the total number of visits to the content page, the total number of visited topics in the content page, and total duration spent in the content page were examined. One hundred forty students in 19 online courses participated in this study. The results of hierarchical linear modeling analyses revealed: (a) Both environmental factors, instructors' autonomy-supportive interaction and learning environments using constructivist-based pedagogy predicted students' autonomous self-regulated motivation (b) Students' autonomous self-regulated motivation predicted students' self-reported engagement, achievement, and satisfaction (c) Two personal factors, interest in the course and technology self-efficacy did not predict students' autonomous self-regulation (d) Students' autonomous self-regulated motivation did not predict any interaction behaviors. The findings from this study are largely congruent with prior theory and research in the fields of academic motivation, self-determination, and online learning, which note that environmental factors, instructors' autonomy-supportive interaction and constructivist-based pedagogy significantly affect students' autonomous self-regulation in online learning environments.

This study examined the electronic discourse in an online course to investigate if culture exhibited itself in the communication of students. The researcher also sought to find out if a third culture was built in this course and if so, what design features facilitated the emergence of this third culture. A graduate-level online course at a Southwestern university was examined in a case study. Computer-mediated discourse analysis was used as the method. The students were administered an online demographic survey to collect information about their background. The online communication of the students, the instructor, and the assistants were analyzed. A semantic analysis matrix was developed based on the pilot study that was used to investigate the content of the messages posted in the discussion conferences. The results showed that culture did not exhibit itself in the discourse. A third culture, however, was formed by the students. The discourse characteristics of this third culture are producing timely and intelligent comments and equal levels of participation; use of materials from both cultures; constant interaction among participants; creating a side conversation between two different cultures; a common discourse accent; words, expressions, acronyms created in the course; curiosity, sensitivity, openness towards otherness, critical engagement with others; and ability to understand and tolerate different perspectives and cultural phenomena. The design features of the particular online course were discussed. The design features that may have helped create a third culture among students are face-to-face meetings and introductions conference in the course; instructor’s teaching strategies such as creating expectations for participation and her scaffolding and mentoring throughout the course; and features of the course communication platform such as the ability to embellish the thoughts through the use of fonts, colors, and quoting.

Many studies report information overload (IO) as one of the main problems students encounter in computer-mediated communication (CMC). To date, researchers have paid little attention to the problem of IO—more specifically, to its impact on students’ quality interaction—in educational CMC. In an attempt to fill that gap, the purposes of this study were as follows: (a) to understand the difficulties students encounter that contribute to their perceptions of IO in CMC, (b) to observe the impact of those difficulties on students’ learning in online discussions, and (c) to identify students’ strategies for avoiding or managing those difficulties in order to engage in quality learning. Interviews with students and computer conferencing transcripts were analyzed both qualitatively and quantitatively. Interviews with 10 graduate students near the beginning of the semester revealed that although students were exposed to the same amount of information in the same learning environments, different individuals experienced different degrees of IO. Varied learner characteristics caused some students to be more susceptible to IO than others. The difficulties students encountered that contributed to their perceptions of IO included connection problems, navigation difficulties, discomfort with online communication, numerous ongoing discussion messages and endless resources, difficulty in organizing learning, and problems understanding the assigned readings. Those difficulties tended not to affect students’ deep processing as observed in their discussion messages, but might influence students’ online interaction with others. Students engaging in quality learning in online discussions were interviewed near the end of the semester to investigate their learning strategies. The results indicated that students used a variety of strategies to deal with those difficulties. Those strategies were related to online class preparation, identifying relevant information, processing online information and printed materials, keeping learning on track, organizing learning, and avoiding internal and external distractions. The results of this study have implications for course design.

Distance learning is one of the most important forces driving educational reform today. Headlines touting the benefits of educational technology are commonplace in journal articles and newspaper and television reports. In the past five years, we have seen an explosion in the number of computer-mediated courses offered at two- and four-year institutions. An area of particular growth has been that of asynchronous online instruction. This growth has spurred the development of a new construct, the study of computer supported collaborative learning (CSCL). Research on collaborative learning within the setting of online instruction is limited and anecdotal at best. Emergent research on CSCL shows learners in this learning environment are not isolated. It is a vibrant network of people--an interactive learning community--that is not limited by time, place, or the constraints of a classroom. Examination of the practical application of this theory is critical, especially when withdrawal rates for distance learning courses can range from 30-80%. Therefore, there is a need to understand the experiences and perceptions of distant learning students and the instructional strategies used by faculty within the online classroom where the social and cultural context of the learning community is created. The purpose of this qualitative study was to observe the pedagogical practices used by online instructors and examine the perceptions and characteristics of those students taking community college online courses. Then, compare this finding to what research says is needed for online instruction to support and foster computer supported collaborative learning. This study focused on four community college instructors who teach web-based courses in English, developmental mathematics, history and management. The study also examined the demographics of the students taking these online courses in order to provide a descriptive background of the participants and to assess their perceptions and experiences. The research questions addressed in this study were: 1) What pedagogical strategies do instructors teaching online courses use?; 2) How do online instructors promote collaborative learning communities?; and 3) What are the learning experiences of the students who take online courses? / text

Investigating howgroups communicate, build knowledge and expertise, reach consensus or collaboratively solve complex problems, became one of the main foci of contemporary research in learning and social sciences. Emerging models of communication and empowerment of networks as a form of social organization further reshaped practice and pedagogy of online education, bringing research on learning networks into the mainstream of educational and social science research. In such conditions, massive open online courses (MOOCs) emerged as one of the promising approaches to facilitating learning in networked settings and shifting education towards more open and lifelong learning. Nevertheless, this most recent educational turn highlights the importance of understanding social and technological (i.e., material) factors as mutually interdependent, challenging the existing forms of pedagogy and practice of assessment for learning in online environments. On the other hand, the main focus of the contemporary research on networked learning is primarily oriented towards retrospective analysis of learning networks and informing design of future tasks and recommendations for learning. Although providing invaluable insights for understanding learning in networked settings, the nature of commonly applied approaches does not necessarily allow for providing means for understanding learning as it unfolds. In that sense, learning analytics, as a multidisciplinary research field, presents a complementary research strand to the contemporary research on learning networks. Providing theory-driven and analytics-based methods that would allow for comprehensive assessment of complex learning skills, learning analytics positions itself either as the end point or a part of the pedagogy of learning in networked settings. The thesis contributes to the development of learning analytics-based research in studying learning networks that emerge fromthe context of learning with MOOCs. Being rooted in the well-established evidence-centered design assessment framework, the thesis develops a conceptual analytics-based model that provides means for understanding learning networks from both individual and network levels. The proposed model provides a theory-driven conceptualization of the main constructs, along with their mutual relationships, necessary for studying learning networks. Specifically, to provide comprehensive understanding of learning networks, it is necessary to account for structure of learner interactions, discourse generated in the learning process, and dynamics of structural and discourse properties. These three elements – structure, discourse, and dynamics – should be observed as mutually dependent, taking into account learners’ personal interests, motivation, behavior, and contextual factors that determine the environment in which a specific learning network develops. The thesis also offers an operationalization of the constructs identified in the model with the aim at providing learning analytics-methods for the implementation of assessment for learning. In so doing, I offered a redefinition of the existing educational framework that defines learner engagement in order to account for specific aspects of learning networks emerging from learning with MOOCs. Finally, throughout the empirical work presented in five peer-reviewed studies, the thesis provides an evaluation of the proposed model and introduces novel learning analytics methods that provide different perspectives for understanding learning networks. The empirical work also provides significant theoretical and methodological contributions for research and practice in the context of learning networks emerging from learning with MOOCs.

With the increasing pace of technological changes in the modern society, there has been a growing interest from educators, business leaders, and policymakers in teaching important higher-order skills which were identified as necessary for thriving in the present-day globalized economy. In this regard, one of the most widely discussed higher order skills is critical thinking, whose importance in shaping problem solving, decision making, and logical thinking has been recognized. Within the domain of distance and online education, the Community of Inquiry (CoI) model provides a pedagogical framework for understanding the critical dimensions of student learning and factors which impact the development of student critical thinking. The CoI model follows the social-constructivist perspective on learning in which learning is seen as happening in both individual minds of learners and through the discourse within the group of learners. Central to the CoI model is the construct of cognitive presence, which captures the student cognitive engagement and the development of critical thinking and deep thinking skills. However, the assessment of cognitive presence is challenging task, particularly given its latent nature and the inherent physical and time separation between students and instructors in distance education settings. One way to address this problem is to make use of the vast amounts of learning data being collected by learning systems. This thesis presents novel methods for understanding and assessing the levels of cognitive presence based on learning analytics techniques and the data collected by learning environments. We first outline a comprehensive model for cognitive presence assessment which builds on the well-established evidence-cantered design (ECD) assessment framework. The proposed assessment model provides a foundation of the thesis, showing how the developed analytical models and their components fit together and how they can be adjusted for new learning contexts. The thesis shows two distinct and complementary analytical methods for assessing students’ cognitive presence and its development. The first method is based on the automated classification of student discussion messages and captures learning as it is observed in the student dialogue. The second analytics method relies on the analysis of log data of students’ use of the learning platform and captures the individual dimension of the learning process. The developed analytics also extend current theoretical understanding of the cognitive presence construct through data-informed operationalization of cognitive presence with different quantitative measures extracted from the student use of online discussions. We also examine methodological challenges of assessing cognitive presence and other forms of cognitive engagement through the analysis of trace data. Finally, with the intent of enabling for the wider adoption of the CoI model for new online learning modalities, the last two chapters examine the use of developed analytics within the context of Massive Open Online Courses (MOOCs). Given the substantial differences between traditional online and MOOC contexts, we first evaluate the suitability of the CoI model for MOOC settings and then assess students’ cognitive presence using the data collected by the MOOC platform. We conclude the thesis with the discussion of practical application and impact of the present work and the directions for the future research.

Der Ansatz des Konnektivismus und die rasante Verbreitung von Massive Open Online Courses (MOOCs) haben eine anhaltende Debatte um die Chancen, Schwierigkeiten und Perspektiven offener Lernnetzwerke in der Hochschulbildung ausgelöst. Die Diskussion reicht dabei vom befürchteten Verlust des Einflusses der Dozierenden als Gewährleister einer kritischen und vielseitigen Auseinandersetzung mit Themen und Lerninhalten, über die lernerseitigen Voraussetzungen für eine erfolgreiche und gewinnbringende Beteiligung an konnektivistischen Kursangeboten, bis hin zur Frage, inwiefern offenes, vernetztes Lernen im institutionell verfestigten Rahmen der Hochschule überhaupt realisiert werden kann. Verlässliche Daten über konnektivistisch ausgerichtete MOOC-Angebote (sogenannte cMOOCs) mit vorrangig studentischer Beteiligung gibt es kaum, da diese im deutschsprachigen Raum bisher vor allem in non-formalen Settings bzw. im Weiterbildungsbereich angeboten und genutzt wurden. Dieser Beitrag stellt zentrale Ergebnisse der Durchführung und Evaluation eines cMOOC mit hauptsächlich studentischen Teilnehmenden vor, der im Sommersemester 2013 und Wintersemester 2013/14 in Kooperation zwischen drei deutschen Universitäten (Dresden, Chemnitz, Siegen) durchgeführt wurde. Der Fokus liegt auf der Frage, in welchem Ausmaß offenes, vernetztes Lernen im Rahmen eines Hochschulkurses ermöglicht werden kann und Lernergebnisse sich identifizieren lassen. Hierzu erfolgt eine Kombination quantitativer und qualitativer Evaluationsdaten.