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Antoniadou, Victoria; Dooly, Melinda – Research-publishing.net, 2017
This chapter aims to answer some of the questions that emerge when carrying out educational ethnography in a blended learning environment. The authors first outline how Virtual Ethnography (VE) has been developed and applied by other researchers. Then, to better illustrate the approach, they describe a doctoral research project that implemented…
Descriptors: Ethnography, Blended Learning, Educational Environment, Educational Research
Moore, Emily, Ed.; Dooly, Melinda, Ed. – Research-publishing.net, 2017
This handbook has been conceived as a guide for young researchers embarking on the exciting journey of investigating different aspects of plurilingual education. The text can also serve to review ideas previously encountered, and perhaps as a means of interrogating research methodologies in plurilingual contexts for those who already have ample…
Descriptors: Qualitative Research, Research Methodology, Multilingualism, Guides
Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – Grantee Submission, 2015
Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…
Descriptors: Educational Environment, Predictive Measurement, Predictor Variables, Cooperative Learning
Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – International Educational Data Mining Society, 2015
Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…
Descriptors: Educational Environment, Predictive Measurement, Predictor Variables, Cooperative Learning
Levy, Sharona T.; Wilensky, Uri – Computers & Education, 2011
This study lies at an intersection between advancing educational data mining methods for detecting students' knowledge-in-action and the broader question of how conceptual and mathematical forms of knowing interact in exploring complex chemical systems. More specifically, it investigates students' inquiry actions in three computer-based models of…
Descriptors: Test Content, Mathematical Models, Prior Learning, Data Processing
Mathies, Charles; Valimaa, Jussi – Tertiary Education and Management, 2013
Recent changes in European higher education have accompanied a strong desire and need by national ministries to have comparable data across institutions and a growing recognition from campus leaders that effective planning and decision-making requires reliable institutional data and analyses. This has induced changes and restructuring of duties…
Descriptors: Higher Education, Institutional Evaluation, Governance, Data Analysis
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers