Microgenetic methods for learning analytics in the R language

Taylor Martin, Phil Janisiewicz, Ani Aghababyan, and Xiaotian Dai, Active Learning Lab, Utah State University

Abstract

This workshop is geared toward introducing our audience to conducting data analyses with R in R Studio. Analyses covered during this workshop include methods such as hierarchical clustering, sequential pattern mining, graphical data interpretation, and Markov Process. The goal of this workshop is to introduce researchers to conducing data analyses in R without much prior knowledge with the programming language and R statistical computing environment.

Main activities

• Introduction to R studio (the length of this introduction will depend on the audience’s familiarity with the environment)
  • Data import
  • Data cleanup
  • Data types
  • Aggregations
  • Visualizations
  • Become familiar with the workshop datasets
  • Conducting analyses
    • Sequence pattern mining in R: running the analysis and interpreting the results
      • aRules algorithm
      • Interpretation of Support
      • Interpretation of expected results
      • Visualizations (e.g., Entropy, Modal etc.)
• Visualizations in R
  • Ggplots
  • Heat maps
  • Customization etc.
• Markov Process (Markov Property and Transition matrix)
  • Generating Markov transition matrix
  • Heat maps
• Clustering
  • Hierarchical clustering
  • Visualizations
  • Interpretation of dendrograms and cluster results

Prerequisites

• Computers
• Internet connectivity
• Install R and R studio (preferably installed beforehand according to this tutorial).

Bios

Dr. Taylor Martin

It is a common belief that doing promotes learning in complex domains like mathematics and science, but there is little research that establishes the validity of this claim. Dr. Martin examines how people learn from doing, or active participation, both physical and social. Currently, is examining how mobile and social learning environments (online and in person) influence content learning in mathematics, engineering and computational thinking using learning analytics methods to understand learning processes at a fine-grained level. Dr. Martin is also Director of the Active Learning Lab at Utah State University, where she supervises eight graduate research assistants and provides leadership over six funded research projects through the National Science Foundation, Utah STEM Action Center, and Pearson NCS. The Active Learning Lab is engaging in cutting edge learning analytics such as: examining how people move through learning environment and how patterns in that movement are related to learning outcomes, developing models of the process of learning in online mathematics environments, and challenging current understandings of how people learn critical mathematics content.

Philip Janisiewicz

Phil Janisiewicz is a Data Scientist for the Active Learning Lab in the Department of Instructional Technology and Learning Sciences at Utah State University conducting research in data management and data modeling. He works with the research teams to investigate and implement new models and techniques for predicting student learning and behavior and inferring the relevance and impact of recommendations and personalized content, using a rich corpus of student data. For more than five years, Phil has been involved in designing and developing databases, web-based applications, analysis methods, and data visualization techniques. The research projects he has participated include projects funded by the U.S. Department of Education, the National Science Foundation, and the Bill and Melinda Gates Foundation. Some of the projects have included data collection across multiple states and by multiple research organizations. He has designed and implemented security and quality assurance measures to meet the highest regulations for data management

Ani Aghababyan

Ani Aghababyan is a PhD Candidate in the Instructional Technology and Learning Sciences (ITLS) program at Utah State University. She holds a B.S. in Law and Political Science from French University in Armenia and she has completed two M.S. degrees, Business Administration and Information Systems at Utah State University. During her masters in Information Systems she worked for Utah State University’s Information Technology department as a programmer and web developer, as well as served as the web master for ITLS department.

Currently, Ani works as a graduate researcher for the Active Learning Lab. Ani’s research interests include educational data mining, learning analytics, digital games for education, and student affect in educational digital games. More specifically she is interested in the patterns of student affect and behavior while they are engaged in educational digital games. She has experience with game back-end data engineering, student performance modeling, & engagement identification during learning. As part of her dissertation, Ani has been investigating the usage of data mining techniques/algorithms to detect student affective states such as frustration and confusion and identify consistent sequential patterns of student affect and engagement.

XiaoTian Dai

Xiaotian Dai is currently working towards his PhD degree in statistics at Utah State University and plans to graduate in May 2016.  He has previously obtained a B.S. and M.S. degree, also in statistics.  His master’s thesis is about processing and manipulation of data collected from the educational on-line game ”Refraction.”  Xiaotian’s research interest is to apply his quantitative and statistical background to our learning analytics research. Xiaotian was also a graduate instructor for elementary math and statistics courses in the Department of Mathematics and Statistics. Currently, he is working as a graduate research assistant in Active Learning Lab.