My Research

How Can We Make Learning Better?

Recent research has resulted in a new book,

Innovative Learning Analytics for Evaluating Instruction: A Big Data Roadmap for Effective Online Learning (Frick, Myers, Dagli & Barrett, 2022).

In our book, we document the extraordinary effectiveness of First Principles of Instruction for promoting online learning.  We used these principles to design the online Indiana University Plagiarism Tutorials and Tests (IPTAT).  Analysis of Patterns in Time (APT) was the primary research methodology for evaluating this MOOC in 2019 and 2020.  We used APT to segment nearly 1.87 million temporal maps and to match learning patterns by leveraging Google Analytics tracking and reporting tools. We then created spreadsheet formulas to compute APT likelihood ratios from the GA results.  We found that successful students worldwide were nearly four times as likely to select instruction designed with First Principles, when compared to unsuccessful learners.

Follow-up Study

Frick, T., Myers, R. & Dagli, C. (2022). Analysis of Patterns in Time for Evaluating Effectiveness of First Principles of Instruction. Educational Technology Research and Development. https://doi.org/10.1007/s11423-021-10077-6

Main findings of this study of 172,000+ learning journeys in spring 2021: Students who chose IPTAT instruction designed with First Principles were nearly 4 times more likely to pass a Certification Test than to fail one.

Analysis of Patterns in Time (APT)

APT can be used as a practical way to evaluate instructional effectiveness.  Applying APT to do learning analytics means big data can be harnessed to evaluate online teaching and learning.  APT is a powerful method for finding meaningful patterns in massive datasets.

Important Theory Development

I continue to develop Totally Integrated Education. TIE is intended to help students form mental structures which integrate cognition, intention, and emotion through grounded real-world experiences. TIE theory predicts that learning activities which increase wholeness in student mental structures should be more effective with respect to student learning.

Integrated knowing that emerges during TIE should result in more strongly connected long-term memories. What learners come to know and believe is predicted to be less likely to be forgotten as time passes.  Recent research findings in neuroscience which explain how learner minds emerge from formation of complex neural networks are consistent with TIE predictions. For a recent overview, see Bertolero & Bassett (July, 2019): How the mind emerges from the brain's complex networks, in Scientific American. For a less technical overview, see Eagleman's (2020) book: Livewired: The Inside Story of the Ever-Changing Brain.

For more on TIE, see a brief overview of TIE development (PDF, 6 pages).

Recent Publications on TIE and Educology

Research Indices

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