Research Methods – Virtual Canuck

The Answerthis.io AI app looks at my Interaction Equivalency Theory

I’m not immune from the relentless hype (and warnings from multiple perspectives) about generalized AI, AI enhanced browsers and AI customized for specific applications.

Therefore, I couldn’t resist spending a few minutes checking out an AI app – Answerthis.io specifically aimed at researchers and students – see https://answerthis.io/

Not being overly modest (to say the least) I decided to ask a question about an idea that I had and paper I wrote over 20 years ago (Anderson, T. (2003). Getting the mix right again: An updated and theoretical rationale for interaction – International Review of Research in Open and Distributed Learning, 4(2).

The paper achieved modest success (1100 citations) which was a bit surprising as I wasn’t really sure there really was a learning equivalency between content interaction; student/teacher interaction; and student-student interaction. Moreover, I wasn’t sure how this ‘theory’ could be empirically validated nor disproved. However, over the years a number of researchers (aided in no small part by my Japanese colleague Terumi Miyazoke) have used it and seems to have been useful – perhaps most noticeably for financially pressed distance education administrators trying to create affordable (and thus accessible) learning opportunities.

So I asked Answerthis.io “What is useful about Anderson’s Interaction equivalency theory?”

It examined 93 papers in a minute or two and came back with more indicators of usefulness than I could remember. The resulting “answer” is as follows:

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“Anderson’s Interaction Equivalency Theory” (also known as the Interaction Equivalency Theorem, or EQuiv) is a significant conceptual framework in online and distance education. At its core, it states that “deep and meaningful learning is supported as long as one of three forms of interaction (student-teacher, student-student, student-content) is at a high level; the other two may be offered at minimal levels or even eliminated without degrading the educational experience.” The theory recognizes that sustaining all forms of interaction at maximum levels may not always be feasible due to constraints such as time, resources, and institutional scale, especially in online or blended environments.

Key Benefits and Usefulness of Anderson’s Theory

Flexible Course Design and Resource Allocation:
EQuiv allows educational designers and institutions to focus resources and instructional efforts where they are most impactful. If strong interaction can be established in one domain (e.g., through rich content or peer collaboration), then the other types may be comparatively minimized without a loss in learning quality. For instance, in very large courses where teacher-student interactions are limited, enhanced student-content or student-student interactions can compensate, enabling scalability without compromising outcomes[1].
Personalization and Adaptation:
The theory provides a rationale to adapt interaction according to the specific needs and circumstances of different learner groups, such as non-traditional or part-time students who may not engage frequently with instructors but can benefit from alternative interaction forms[2].
Practical Framework for Blended and Online Learning:
It offers educators a guide to make informed decisions on which instructional tools or approaches to use, justifying, for instance, the use of Learning Management Systems (LMS) to enhance interaction and accessibility, allowing for key educational goals (like participation and learning engagement) to be met even if instructor interaction is limited[1].
Supports Student Satisfaction and Achievement:
Empirical and conceptual studies support the application of EQuiv in raising learner satisfaction, reducing attrition risk in distance education settings, and increasing course completion rates particularly when strong “interaction presence” is maintained in at least one channel[2].
Guides Risk Identification and Intervention:
The theory isn’t just for course designers—it can be used by institutions to develop risk profiles of students potentially at risk of dropping out, enabling targeted interventions where interaction is lacking[2].
Extends Beyond Structured Courses:
Although EQuiv originated in distance and online learning, it has potential applications in other higher education contexts, such as the master-apprentice model in doctoral education. By compensating for limited supervisor (S2T) interaction with enhanced content or peer interactions, EQuiv addresses issues of isolation and supports deeper learning[2].
Efficient Use of Learning Technologies:
By focusing on maximizing one type of interaction, it provides a rationale for the use of asynchronous content, peer forums, or automated feedback systems—tools essential for scaling and accessibility in digital education[1].
Framework for Ongoing Research and Development:
The theory has stimulated further research into interaction dynamics, informing both empirical investigations and theoretical development around how different forms of interaction contribute to learning, motivation, and retention in virtual environments[1], [2].
Facilitates Deep and Meaningful Learning:
The focus on ensuring at least one robust interaction provides conceptual and empirical support for deep cognitive engagement, even when all three modes are not maximally present. This is crucial for the design of effective massive open online courses (MOOCs) and large-scale distance education where teacher-student contact is inherently limited.
Guides Implementation of New Educational Models:
EQuiv justifies the structural design decisions in “flipped classrooms,” blended courses, and synchronous/asynchronous online programs, providing a theoretical foundation for deliberate reduction or enhancement of interaction modes based on course goals, audience, and technological affordances[1].

In summary, Anderson’s Interaction Equivalency Theory is useful because it offers a flexible, evidence-based, and scalable framework for online, blended, and distance education. By clarifying how and where to invest instructional resources, it enables educators and institutions to optimize learning outcomes, enhance satisfaction, reduce attrition, and adapt to diverse learner needs across multiple educational contexts[1], [2“

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My takeaways:

The tool does a credible and amazingly thorough job that seems quite accurate.
It shows that assigning tasks such as this as an assessment activity in a senior undergrad or grad course hardly seems worth either keep student learning nor the time and effort taken by a teacher trying to assess it. I have had graduate students create blog and wiki entries on major ideas in distance education in the past, but given these tools, think it is an exercise in continuously “reinventing the wheel”.
For a busy scholar (or student) trying to get up to speed on ideas from the literature that they may have missed or need a refresher, the tool is remarkably both efficient and effective.
The tool doesn’t yet have much of a revenue model- but no doubt that will come.

For the past two months, I’ve been occupied with a qualitative study of teachers’ use of digital technology in Alberta Schools. The study is sponsored by the Alberta Teachers’ Association. It has been very useful for me to get down to actually doing a full scale qualitative study after years of teaching grad students research methods courses and advising and supervising graduate students.

This post is to highlight (and celebrate) two great tools that we have used that I think have almost revolutionary, or at least potentially disruptive effect on interview based research.

For this study we conducted 19 interviews mostly via Skype or Google Hangout, but also face-to-face and two on the old-fashioned telephone. We recorded the interviews using a variety of digital tools included with Skype or Hangout or recording apps on our machines. We also used a portable digital recorder as backup. The MP3 recordings were then uploaded to new tool – Trint.com

Trint is an automatic transcription tool, which converts the audio track to text. We uploaded the approx. 50 minute recordings and received notification about 15 minutes later when they had been transcribed to English texts. As expected, the transcription failed (and sometimes comically) at transcribing proper nouns and other slang or colloquial terms relevant only to specialized audiences. The genius of the system is the editor within which the new text transcription is then displayed. This specialized editor has an audio track at the bottom, that reads aloud the audio track. You can vary the audio playback speed that then highlights on the screen the first draft transcription. This editor allowed us to add speaker’s name, insert or delete paragraph breaks, search and replace, delete extraneous chatter and of course has a built-in spell checker. The initial editing took us about the same length of time as the recording runs. So, we went from an Mp3 Audio recording of 50 minutes of clean text in about an hour.

We did however notice significant variation in the accuracy of the transcription and thus the length of time needed to manually edit the transcription. The recordings that were done through Skype or Hangout, were very accurate and required minimal editing. Those we recorded on the phone, produced much worse transcription, requiring us to edit and re edit as we listened to the actual interview. Thus, as Trint notes on their home page, the quality of the recording is critical to success.

Trint has an interesting pricing system, that after the first $10 free credit, charges are based upon the length of the transcription submitted. The cost is $15 US per hour, but we were pleasantly surprised that the cost was significantly lower than the actual length of our recordings. So this was money very well spent compared to shipping the transcripts to India or hiring professionals here in Canada. I’ve heard that professional cost for transcription can easy mount to over $300 per hour.

Once we had the text transcript, we uploaded to the cloud-based analysis program Dedoose. Dedoose.com, is not a new program, but has some interesting features that are certainly an improvement on my earlier work with Atlas and NVivo.

First, Dedoose is designed for coding teams. It has extensive tools for training and then testing inter-coder reliability. Next, is the general ease (not too bad a learning curve) of the coding itself. We were quite easily able to code, create new codes, arrange them in families and other functions of high quality qualitative analysis tool set. Finally, Dedoose’s has an integrated suite of quantitive tools. These allow you to look at any significant differences between subjects based on a host of ‘descriptors’ such as gender, school size or whatever variables the research chooses to associate with each of the interviewees. Of course, such quantitative analysis is only meaningful if the coding is done systematically and reliably – a challenge to the very epistemological validity of the subjective experience of qualitative coding. Nonetheless, Dedoose worked as advertised and the coding was straight forward. Retrieval of the code excerpts to a Word file was also quite easy.

Dedoose also has an interesting pricing system. An account is billed (after the free trial) at $14.95 (US)/month – BUT only for the months in which the program is used. I was pleased to see the system remembered my account from over 2 years ago- and I am awaiting to see my monthly charge appear on my credit card.

Now I am looking for a program that writes up the research, recommendations and implications for practice and further research.