Learning with Tablet PCs Research Agenda: From Facts to Pragmatics

Here’s the draft I promised of my prepared comments for the WIPTE 2008 panel Wednesday, October 16, 2008, at Purdue University.

1. ABSTRACT AND INTRODUCTION

These comments review aspects of a proposed research agenda to provide data points that assess learning with Tablet PCs. The goal is to provide a database for Tablet PC mediated learning and to better understand performance of Tablet features in this learning. Each point measures the extent to which a learner uses empirically established learning principles to complete a lesson successfully. These data index a kind of feedback teachers use to adapt lessons for prompt increases in student learning. These data may also be valuable for identifying ways to improve Tablet based educational software and to identify ideal combinations of Tablet features in a learning setting. We also suggest that WIPTE participants form a working group to draft a model research program about learning with Tablet PCs and ways to distribute and coordinate this research across existing and future projects.

I want a Tablet PC to automate what I do in my head when observing choices a learner makes to solve a problem. Learners and teachers can use feedback from automated observations to increase learning efficiency and decrease learning loss. I call this an applied learning behavior analysis with Tablets (ALBAT).

To that end, I propose a vertically integrated research agenda that may be distributed across existing and future projects in multiple locations. It will challenge behavior learning specialists and software engineers to combine human learning and machine operating principles for reliable, automated, databased learner feedback.

2. PROBLEM STATEMENT AND CONTEXT

If learning with Tablets is to move from offering state-of-the-art learning practices to a state-of-school-learning-tool, it will likely rely on experimental, empirical answers to such questions as, “What do I do now?” and “What will it cost me to do it?” I call these answers first-order descriptions of learner behavior patterns. Some call them technical descriptors of the mechanics of learning. As a teacher, I use them to monitor how a learner successfully manages unfamiliar information and skills. These learning principles exist independent from lesson content. A database of individual learner behavior patterns will allow elegant, powerful, and easy generalizing to Tablet based pedagogy.

2.1 Problem. An empirical database does not exist that describes behavior patterns people use to learn with Tablets. A comprehensive literature review of behavior learning principles used with Tablets does not exist. Also, a research agenda to establish such an empirical database does not exist. The proposed agenda will add a learners’ view to the research literature and to the beginning of a database. These data should describe behavior based learning principles used with Tablets.

2.2 Related Literature. Behavioral scientists have assembled a broad body of experimental, empirical, databased learning principles during the past century. People such as Binet, Kirk, Skinner, Terman and Merrill, Thorndike, Thurston, and Zeaman and House, to cite a few, and their intellectual descendents have provided a reliable base for understanding observables of learning. Researchers, including Bijou, Gold, and Lindsley, have demonstrated ways to use these findings to increase learning. References to their work appear at least by implication in Tablet related literature about Tablets in schools and are used implicitly in some education software designs.

The proposed agenda suggests a review of behavior learning literature as a foundation for designing, conducting, and interpreting study results. This review will distinguish among what I call first, second, third, and fourth order behavior principles of how people learn with Tablets.

2.3 Available Potential Data. Uncounted thousands of students have used Tablet PCs and their Ink and TouchScreen cousins for an uncounted likely hundreds of millions of transactions to manage new skills and information in and out of schools. Educators and others have described uses of Tablets in schools sufficiently to illustrate the potential for assembling a research agenda that delineates how people learn with the Tablet family. Most of these observations appear based on teacher views that assess the impact of Tablets on academic performance.

I want a software program to capture and analyze data from Tablet transactions in order to describe how students learn with Tablets. For example, Kamin (200_) describes a dashboard that indicates such captures can occur and impact learning. Classroom Presenter (Anderson, 200_), DyKnow Vision (), and the MathPractice family (Heiny, L., 200_) of Tablet software appear to indicate that recording, analyzing, and reporting various user frequency counts is possible.

In the proposed research, learning with a Tablet means that a person uses observable behavior patterns to solve a problem successfully. That effort includes trial-and-error patterns until identifying the correct way to solve the problem.
This leads to the challenge to identify Tablet capacity to analyze user input. An outstanding question is: Do Tablet feature error rates, such as with handwriting and speech recognition, permit significant value of automated analysis of user inputs over demographic groups of learners?

3. Solution Proposed

This research will examine learning with Tablets from a view of learners. It will study this view at four levels. Researchers will monitor behavior patterns while learners complete various tasks with Tablets, including academic school assignments.

Learners implicitly ask eight questions to learn something. Researchers have identified learning principles that address some of these questions.
A proposed starting strategy is to leverage simple color and form assessment protocols used with preschool learners. These sample sets permit establishing procedures to define an operational threshold for when learning with Tablets begins to exist as reliably identifiable. Assessment developers refer to this as establishing a basal level. Engineers can use it to satisfy criteria for a successful Alpha test determining minimum capacities of Tablets to analyze user input.

3.1 Learners’ Questions

Implicitly, learners ask a cascade of questions until they learn whatever teachers ask. These questions sample relationships across questions and learners’ increasing probing to clarify specifics required to meet learning criteria.
When faced with an unknown, learners implicitly start with two common sense questions: “What do I do now?” and “What will it cost me (in time, effort, other intangibles, and tangibles) to do it?”

Learners then ask six more questions to search for answers to the what-to-do and the personal-cost questions. These six seem a reasonable starting point for a research agenda about learning with Tablets until empirical data indicate an alternative place to start. I’ll delineate one of these to indicate that behavioral learning literature provides templates and procedures to guide studies to describe how people learn with Tablets. A full research proposal will sample all sets of questions.

Q1: What will I learn? The first thing a learner must do to answer this question is figure out what to learn. Does someone tell me what to learn or must I figure it out through trial-and-error? If the latter, then will it likely be an answer to one of these four generic questions? (Terman and Merrill (1960) said they used these questions to construct and revise the Stanford-Binet Intelligence Scale.) Do I use free recall to come up with its name or its use? Do I say (do) what someone else says (does)? Do I choose from a range of options someone offers, or Do I guess, based on what I already know, if I can figure out how what I know is relevant?

More questions follow to clarify what to learn. These questions seem self explanatory as do follow-up questions each of them triggers. For example, What is it? Triggers another string of questions: Will I see “it” on the screen or listen to the audio, or find “it” when I use the pen? In other words, do I see “it,” hear “it,” or feel “it”? Then, when I find “it,” what do I name “it” or how do I describe or demonstrate “its” use.

After addressing what stimulus to attend, learners ask related questions about What is “it” the same as? What is “it” not like? What is missing from what I see? What comes after what I hear?

Q 2 – 6: These questions follow the same pattern of cascading triggers: How will I learn it? How do I know I learned it? How do I show I learned it? What will it cost me to learn it? And, So what do I get for learning it?

3.2 Precipitating Research Questions

These learner questions lead to a series of potential research questions about using Tablets. For example, What do people do to learn with Tablet PCs? Do people learn more with Tablets than with paper-and-pencils? Do people learn more with the keyboard or with a pen/stylus? Do people learn with Tablets at home the same as at school? Do people with different backgrounds learn with Tablets the same way?

3.3 Research Procedures

This agenda includes a shorter and a longer term series of studies. The shorter term gives priority to demonstrating proof of concept that Tablets can analyze and report learner input to complete academic assignments reliably and usefully in real time. Longer range studies translate and examine the utility of these capacities in classrooms and their implications for various schooling practices and policies.

A review of the shorter term studies illustrates necessary components for four sets of vertically integrated descriptions of learning with Tablets. Research to establish proof of concept begins with describing first order learning principles then moving to second-order learning principles used with Tablets.

3.3.1 First-Order Learning Principles Studies – These studies will give priority to identifying the most simple, minimal behavior patterns learners use. Study designs will replicate, elaborate, and supplement controlled empirical experiments to describe dimensions of attention by Zeaman and House (1963). These studies will yield descriptions of priorities Tablet learners give to visual, auditory, and haptic stimuli inputs to complete assigned tasks. Without learners exhibiting these behavior patterns, observers will likely agree that intended learning has not occurred. Researchers will also likely identify which Tablet features reveal additional learning principles. Together, these data will provide baselines about ways to yield more efficient learning with Tablets.

3.3.2 Second-Order Learning Principles Studies – These principles describe ways learners scale-up first-order principles to complete in a controlled setting, for example, a classroom math assignment.

Taken together, first through fourth order learning principles provide data points for analyzing and reporting learning rates across features of the Tablet family.

4. Evaluation / Assessment

The criterion for utility of this proposal rests with the elapsed time taken to establish an empirical experimental database that allows Tablets to analyze and report learner behavior in real time to individual learners and teachers. It appears possible to reach the proposed goal within two years through programmed research. Likely, an ad hoc arrangement among researchers will take longer to reach a useful approximation of the goal.

5. Implications, Discussion, and Future Work

Instructors may use these descriptions to adjust lessons with measured confidence in order to increase student learning rates. Software developers may use these benchmarks to design more efficient Tablet family learning programs. Educators and parents may use them to evaluate in order to decide which Tablet features and software to buy. Policy makers may use these data to assess the relative utility of regulations, appropriations, and pedagogy for increasing student learning rates with mobile PCs.
5.1 Implications. Here’s a sample of implications:
To an extent that a Tablet automated adaptive learning behavior analysis feedback system increases individual learning rates, learners may learn more, faster, and with less cost than with paper-pencil and group instruction. This potential fact will provide learners and teachers with learning and pedagogy choices unavailable previously. In turn, boards of education may examine resource distributions across budget lines against these choices.

5.2 Discussion. This research agenda addresses how learners benefit by using Tablet PCs. Understanding this fundamental is core to advancing descriptions of how people learn with Tablets, to aid in deciding which Tablet to purchase for which purpose, and to determine corporation investments in future Tablet features.

In addition, this agenda has the potential to assist researchers with various interests to coordinate their studies as if they participated in a research program. Coordination can permit quicker measurements of changes in learning rates by Tablet users than through individual efforts by researchers, manufacturers, and developers.

5.3 Future research. Scientists may examine a related series of questions about learning with Tablets:

For whom does automated feedback with Tablets increase learning rates?

What changes in Tablet technology might increase learning rates more?

Can Tablet capacity also allow calculation of learning loss rates, or does that require more than just figuring the value of errors learners make during an assignment?

To what extent can automated real time feedback to learners using Tablets free educators to address other classroom activities?

Does learning with Tablets suggest reexamining assumptions about what constitutes learning principles and their uses?

Which of study data indicate adjustments in pedagogy irrespective of using Tablets?

Together, this proposed research agenda offers a way to combine what is known about how people learn with how engineers design software to yield measured confidence in increasing learning with Tablet PCs.

References

Zeaman, D. & House, B.J. (1963). The role of attention in retardate discrimination learning. In N.R. Ellis (Ed.), Handbook of mental deficiency: Psychological theory and research. New York: McGraw-Hill.

Acknowledgments

Credits and appreciation for significant assistance with the formulation across decades of ideas in these comments rest with Joseph J. Cunningham, Bill Ferriter, Marc Gold, Lora Heiny, Loren Heiny, Layne Heiny, Todd Landstad, Robert Scoble, Travis Wittwer, Kenneth Wyatt, and hundreds of comment writers on teacher blogs. Errors and weaknesses remain faults of the author.

This edition of my comments extends previous posts and complements an evolving FAQ. A PDF version of each will be available for download soon at tabletpcpost.com. They will include more references and another graphic.