SITE Joint SIG Symposia: A Collaboration Between the K-12 Online Learning SIG and Distance Learning SIG: How Higher Education and K-12 Online Learning Research Can Impact Each Other

Facilitated by Rick Ferdig of Kent State University and editor of JTATE, this Symposia brings together the work of the K-12 Online Learning SIG and the Distance Learning SIG communities and focuses on presentations from scholars in the field whose work has implications for both higher education and K-12 online learning. This Symposia will have nine panelists who will each present their work and then talk specifically about how their work can inform both K-12 and HE. Included in the list of Higher Education-focused panelists are Trisha Litz of Regis University, Maggie Niess of Oregon State University, Antoinette Davis of Eastern Kentucky University, and David Marcovitz of Loyola University Maryland. Included in the list of K-12-focused panelists are Leanna Archambault of Arizona State University, Kerry Rice of Boise State University, Michael Barbour of Touro University, Amy Garrett Dikkers of the University of North Carolina at Charlotte, and Aimee Whiteside of the University of Tampa

SITE Joint SIG Symposia: A Collaboration Between the K-12 Online Learning SIG and Distance Learning SIG: How Higher Education and K-12 Online Learning Research Can Impact Each Other

Facilitated by Rick Ferdig of Kent State University and editor of JTATE, this Symposia brings together the work of the K-12 Online Learning SIG and the Distance Learning SIG communities and focuses on presentations from scholars in the field whose work has implications for both higher education and K-12 online learning. This Symposia will have nine panelists who will each present their work and then talk specifically about how their work can inform both K-12 and HE. Included in the list of Higher Education-focused panelists are Trisha Litz of Regis University, Maggie Niess of Oregon State University, Antoinette Davis of Eastern Kentucky University, and David Marcovitz of Loyola University Maryland. Included in the list of K-12-focused panelists are Leanna Archambault of Arizona State University, Kerry Rice of Boise State University, Michael Barbour of Touro University, Amy Garrett Dikkers of the University of North Carolina at Charlotte, and Aimee Whiteside of the University of Tampa

Data-Driven Modeling of Engagement Analytics for Quality Blended Learning

Engagement analytics is a branch of learning analytics (LA) that focuses on student engagement, with the majority of studies conducted by computer scientists.Thus, rather than focusing on learning, research in this field usually treats education as a scenario for algorithms optimization and it rarely concludes with implications for practice. While LA as a research field is reaching ten years, its contribution to our understanding of teaching and learning and its impact on learning enhancement are still underdeveloped. This paper argues that data-driven modeling of engagement analytics is helpful to assess student engagement and to promote reflections on the quality of teaching and learning. In this article, the authors a) introduce four key constructs (student engagement, learning analytics, engagement analytics, modeling and data-driven modeling); b) explain why data-driven modeling is chosen for engagement analytics and the limitations of using a predefined framework; c) discuss how to use engagement analytics to promote pedagogical reflection using a pilot study as a demonstration. As a final remark, the authors see the need of interdisciplinary collaboration on engagement analytics between computer science and educational science. In fact, this collaboration should enhance the use of machine learning and data mining methods to explore big data in education as a means to provide effective insights for quality educational practice.Peer reviewe

Data Mining in Online Professional Development Program Evaluation: An Exploratory Case Study

This case study explored the potential applications of data mining in the educational program evaluation of online professional development workshops for pre K-12 teachers. Multiple data mining analyses were implemented in combination with traditional evaluation instruments and student outcomes to determine learner engagement and more clearly understand the relationship between logged activities and learner experiences. Data analysis focused on the following aspects: 1) Shared learning characteristics, 2) frequent learning paths, 3) engagement prediction, 4) expectation prediction, 5) workshop satisfaction prediction, and 6) instructor quality prediction. Results indicated that interaction and engagement were important factors in learning outcomes for this workshop. In addition, participants who had online teaching experience could be expected to have a higher engagement level but prior online learning experience did NOT show a similar relationship

Serious Gaming Analytics: What Students´ Log Files Tell Us about Gaming and Learning

In this paper we explore existing log files of the VIBOA environmental policy game. Our aim is to identify relevant player behaviours and performance patterns. The VIBOA game is a 50 hours master level serious game that supports inquiry-based learning: students adopt the role of an environmental consultant in the (fictitious) consultancy agency VIBOA, and have to deal with complex, multi-faceted environmental problems in an academic and methodologically sound way. A sample of 118 master students played the game. We used learning analytics to extract relevant data from the logging and find meaningful patterns and relationships. We observed substantial behavioural variability across students. Correlation analysis suggest a behavioural trade that reflects the rate of “switching” between different game objects or activities. We were able to establish a model that uses switching indicators as predictors for the efficiency of learning. Also we found slight evidence that students who display increased switching behaviours need more time to complete the games. We conclude the paper by critically evaluating our findings, making explicit the limitations of our study and making suggestions for future research that links together learning analytics and serious gaming

Research and History of Policies in K-12 Online and Blended Learning

This chapter provides a historical review of U. S. education policy from its earliest inception to the present day with a focus on policy developments in the 21st century that have influenced the growth and development of online and blended education and those that we can foresee will have the greatest impact moving forward. 21st century policies are synthesized into themes of Online and Distance Learning, Accountability, Innovation and Reform, and Teacher Preparation

ESTIMACIÓN DE CALIDAD DE OBJETOS DE APRENDIZAJE EN REPOSITORIOS DE RECURSOS EDUCATIVOS ABIERTOS BASADA EN LAS INTERACCIONES DE LOS ESTUDIANTES

Open educational resources have emerged as one of the cornerstones ofopen education. One of the main barriers hampering their use and adoption is the lack of sustainable and effective quality control mechanisms in digital repositories. Evaluation strategies such as peer review have not been sufficiently scalable to keep up with the fast pace of open content creation by the user community. This study presents a new approach grounded on learning analytics in order to estimate the quality of learning objects based on the interactions that students have with them in open environments. For the study, 146291 sessions of student interactions with 256 learning objects distributed through an open repository were analyzed. A total of 11 studentlearning object interactions were considered in the study. The quality of the resources was measured using the standard evaluation instrument LORI (Learning Object Review Instrument). To study the relationships between the student interactions with the learning objects and their quality as well as to build a predictive metric, linear regression analyses were used. The results show that there is a relationship between interactions and quality, and that it is possible to estimate with a moderate error the quality of the learning objects based on the interactions that students have with them. The results obtained point out that the proposed learning analytic can be used in open learning object repositories to automatically detect conflicting or low quality resources.Los recursos educativos abiertos se han erigido como uno de los pilares fundamentales de la educación abierta. Una de las principales barreras que está obstaculizando su uso y adopción es la carencia de mecanismos de control de calidad efectivos y sostenibles en los repositorios. Estrategias de evaluación como la revisión por pares no han resultado lo suficientemente escalables para afrontar el ritmo de creación de materiales abiertos por parte de la comunidad. El presente estudio muestra una nueva estrategia basada en analíticas de aprendizaje para estimar la calidad de los objetos de aprendizaje en base a las interacciones que los estudiantes tienen con ellos en entornos abiertos. Se analizaron 146.291 sesiones de interacción de estudiantes con 256 objetos de aprendizaje distribuidos a través de un repositorio abierto. Un total de 11 interacciones estudiante-objeto de aprendizaje fueron consideradas en el estudio. La calidad de los recursos fue medida empleando el instrumento estándar de evaluación LORI (Learning Object Review Instrument). Para estudiar las relaciones entre las interacciones de los estudiantes con los objetos de aprendizaje y la calidad de los mismos y para construir una métrica de predicción se utilizaron análisis de regresión lineal. Los resultados muestran que existe relación entre las interacciones y la calidad, y que es posible estimar con un error moderado la calidad de los objetos de aprendizaje en base a las interacciones que los estudiantes tienen con ellos. Los resultados obtenidos señalan que la analítica de aprendizaje propuesta puede ser utilizada en repositorios de objetos de aprendizaje abiertos para detectar automáticamente recursos conflictivos o de baja calidad

Big data and educational research

Big data and data analytics offer the promise to enhance teaching and learning, improve educational research and progress education governance. This chapter aims to contribute to the conceptual and methodological understanding of big data and analytics within educational research. It describes the opportunities and challenges that big data and analytics bring to education as well as critically explore the perils of applying a data driven approach to education. Despite the claimed value of the increasing amounts of large and complex data sets and the growing interest in making sense of them there is still limited knowledge on big data and educational research. Over the last decades, the developments on information and communication technologies are reshaping teaching and learning and the governance of education. A broad variety of online behaviours and transactional data is (or can be) now stored and tracked. Its analysis could provide meaningful insights to enhance teaching and learning processes, to make better management decisions and to evaluate progresses –of individuals and education systems. This chapter starts by defining big data and the sources and artefacts collect, generate and display data. In doing so it explores aspects related to data ownership and researchers’ access to big data. It then assesses the value of big data for educational research by critically considering the stages involved in the use of big data, providing examples of recent educational research using big data. The chapter is not meant to provide the “how to” details of the analysis of big data. Instead, it aims to offer a pragmatic perspective and to highlight the necessity for educational researchers to acquire computational and statistical skills and to engage with interdisciplinary work to deal with big data, and, at the same time, abide a sociological mind. The chapter also highlights research areas that can be explored to augment our understanding of the role of Big data in education

The Impact Of Student Mindsets In The Virtual Math Classroom

Mindset has been studied in multiple traditional school settings, but its interaction with transactional distance in a virtual school environment is missing from the current research. This dissertation explores the experiences of students and learning coaches in a virtual high school through a series of interviews in order to present a better understanding of how students and learning coaches perceive the role of mindset and transactional distance in their interactions with each other, the content, and the teacher. The case study design applied the lenses of Transactional Distance Theory and Mindset Theory to descriptive coding of interview transcripts and relevant documents and concluded that transactional distance, while at least partially constructed by the student and enabled by the learning coach, contributes to the student’s sense of isolation, the student’s reliance on the learning coach, the increased need for a student to be able to function autonomously and exhibit a growth mindset, and the increased demands on the learning coach above what was initially intended in the virtual model design for that role

A methodology to predict community college STEM student retention and completion

Numerous government reports point to the multifaceted issues facing the country\u27s capacity to increase the number of STEM majors, while also diversifying the workforce. Community colleges are uniquely positioned as integral partners in the higher education ecosystem. These institutions serve as an access point to opportunity for many students, especially underrepresented minorities and women. Community colleges should serve as a major pathway to students pursuing STEM degrees; however student retention and completion rates are dismally low. Therefore, there is a need to predict STEM student success and provide interventions when factors indicate potential failure. This enables educational institutions to better advise and support students in a more intentional and efficient manner. The objective of this research was to develop a model for predicting success. The methodology uses the Mahalanobis Taguchi System as a novel approach to pattern recognition and gives insight into the ability of MTS to predict outcomes based on student demographic data and academic performance. The method accurately predicts institution-specific risk factors that can be used to better retain STEM students. The research indicates the importance of using community college student data to target this distinctive student population that has demonstrated risk factors outside of the previously reported factors in prior research. This methodology shows promise as a mechanism to close the achievement gap and maximize the power of open-access community college pathways for STEM majors --Abstract, page iv