Assessing and Enhancing Knowledge Workers’ Meta-Documentation and Self-Testing: A SSHRC Grant Proposal

I recently applied for a 2-year SSHRC grant to study some of the problems with which Cognitive Productivity is concerned, such as

  1. the challenges knowledge workers face (a) in learning with technology and more generally (b) processing knowledge resources with technology;
  2. the effectiveness of proposed solutions to these challenges.

It is important to study these issues because we depend on knowledge workers to specify and solve humanity’s most critical and complex problems! These people are the “engine” of the knowledge economy.

Dr. Geneviève Gauthier would be a collaborator on this project. She is an Assistant Professor at the Department of Educational Psychology and the Associate Director at the Centre of Teaching and Learning of the University of Alberta.

Here’s an excerpt from the proposal (converted to HTML via markdown). Grant proposals are very different from blog posts; so you might find the following a bit dry.

1. Beginning of Excerpt from Grant Proposal

1.1 Objectives

Most technical sources on which knowledge workers depend for their learning and knowledge building are in electronic format. Software for reading, viewing and listening to electronic knowledge sources, i.e., for processing information, still has significant cognitive limitations (Beaudoin 2013, 2014a). For example, web browsers don’t natively support annotation and the major productivity suites do not include software to help users master knowledge through self-testing. Knowledge workers are not necessarily aware of these limitations nor of ways to overcome them grounded in learning science (e.g., Winne, 2001). In Cognitive Productivity, Beaudoin (2014a) postulated challenges that knowledge workers face when processing electronic resources for deep comprehension, problem solving and mastery, and strategies to address them. The overarching objective of this proposal is to test and enhance this theory of information processing with technology (IPwT), focusing mainly on four proposed contributors to cognitive productivity:

  1. inline annotating of sources (e.g., adding comments to PDF files);
  2. creating and editing meta-docs, i.e., documents about sources (e.g., a MS Word document about a podcast or seminar);
  3. meta-accessing, i.e., storing and retrieving meta-docs; and
  4. self-testing. The first three of these are meta-documentation.

In particular, I will:

  1. interview and survey knowledge workers to assess their strategies for and knowledge about meta-documentation, self-testing and related forms of IPwT.
  2. Add anonymized, tracing to existing potent meta-documentation apps to gather accurate, real-time data about consenting knowledge workers’ IPwT to measure the validity of the survey and paint a precise picture of knowledge workers meta-documentation.
  3. experimentally study knowledge workers — using the aforementioned apps and enhanced training materials we will develop based on the interviews and the updated theory of cognitive productivity—to test hypotheses about IPwT.

This project is meant to discover new facts and concepts about, and yield a better understanding of, knowledge workers’ IPwT. It will help determine whether the updated theory of cognitive productivity can improve meta-documentation and increase judgments of perceived IPwT self-efficacy in knowledge workers.

1.2 Context

We depend on knowledge workers to specify and solve humanity’s most complex problems. Knowledge workers process numerous knowledge sources in their quest to become more effective (i.e., to learn), to solve problems and design products (including new knowledge). The efficiency and effectiveness with which they process knowledge to these ends contributes to their cognitive productivity. IT supports but it can also hinder cognitive productivity. Knowledge workers use ‘productivity’ apps, blogs and books. Yet the concept of individual productivity has received little explicit attention in psychology. So, ironically, knowledge workers (including academics) turn to productivity books that eschew research, such as Getting Things Done (Allen, 2001). “[Allen] didn’t need empirical evidence. […] What he really wants, more than anything else, is to spread the gospel he has discovered – the gospel of GTD.” (Keegan, 2007). As a counter-balance, it is important that scientific research on IPwT address productivity concerns (cf. Ch. 1 of Beaudoin, 2014).

Many students have difficulty learning with IT. This is partly due to (a) IT having poor pedagogical utility (Bratt, 2009 ), i.e., not being designed to exploit potent principles of learning; and (b) those students using inapt learning strategies (Mulcahy-Ernt & Caverly, 2009; Rouet 2006). Related problems are also mooted informally by knowledge workers who complain about their ‘information overload’ (Carr, 2011). However, while there is increasing interest in students’ IPwT (e.g., Bélanger, 2010), scholarly publications rarely address experts’ IPwT, such as how they organize and access their e-documents (Oh & Belkin, 2014), classify their annotations, extract information from sources or deliberately practice (self-test) using this information. In particular there are no published rigorous cognitive task analyses of these functions in experts (Clark et al., 2008; Hoffman & Militello, 2012). This limits the set of potentially helpful concepts and strategies psychologists might research in novices and students, and disseminate to the public. For example, Levitin’s (2014) popular attempt to help knowledge workers use cognitive psychology to “[think] straight in the age of information overload” did not deal with or cite papers on knowledge workers’ IPwT as defined here. He deferred extensively to David Allen! Cognitive science must do better.

My proposed research is based on a detailed analysis of tasks involved in knowledge workers’ IPwT (Cognitive Productivity) and the psychological principles underpinning them. Chapter 3 and Part 3 of Cognitive Productivity identified major shortcomings in IT that, based on lack of explicit solutions on the web and other factors, I predicted even knowledge workers would find challenging to overcome. These predictions led to the four hypotheses listed below. This research will collect empirical data to test and improve the theory. It will focus on three major components of cognitive productivity: generating and accessing meta-docs, annotating meta-docs, and productive practice.

Two of the most established findings about expertise are that experts (1) rapidly and accurately categorize, store and access knowledge, and (2) deliberately practice (Ericsson, 2008). This suggests that improving how knowledge workers categorize and access annotations can improve their productivity. However, this has hardly been examined.

I plan to conduct studies that test the following hypotheses derived from Cognitive Productivity (including their SRL). This will provide information about what knowledge workers know and what their IPwT strategies are. It will characterize their strengths and weaknesses. It will help assess whether suggestions grounded in SRL, related science, and productivity concepts, have the desired effects and in what magnitude. It may also reveal side effects, either positive or negative.


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With thanks to the following people for reviewing this proposal.

  • Carol Woodworth,
  • Geneviève Gauthier,
  • James Cullin,
  • John Nesbit,
  • Phil Winne, and
  • Sarah Mark.

And to many administrators at SFU for helping with various parts of the application.

Please feel free to share your thoughts about this project!

Published by

Luc P. Beaudoin

Head of CogZest. Author of Cognitive Productivity books. Co-founder of CogSci Apps Corp. Adjunct Professor of Education, Simon Fraser University. Why, Where, and What I Write. See About Me for more information.

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