Athabasca University School of Computing and Information Science Effects of Individual Differences and Visual Information Seeking on Learning Strategies in Essay Writing Nathan Laan Athabasca University School of Computing and Information Science

Nathan Laan - Bio IT Applications Manager for the Edmonton Police Service. Background in Software Architecture, Design, and Development. Working in Software Development and IT for 17 years. Live in Edmonton Alberta with my wife and three kids, aged 3 through 9. Education includes a BSc in Computing Science.

Supervisory Committee Dr. Dunwei Wen Associate Professor, School of Computing & Information Systems, Athabasca University http://scis.athabascau.ca/scis/staff/faculty.php?id=dunweiw Dr. Dragan Gasevic Professor, Schools of Education and Informatics, University of Edinburgh http://www.inf.ed.ac.uk/people/staff/Dragan_Gasevic.html Dr. Phil Winne Associate Dean, Graduate Studies in Education, Simon Fraser University Professor, Faculty of Education, Simon Fraser University http://www.sfu.ca/education/faculty-profiles/pwinne.html

Purpose Investigate effects of (1) individual differences and (2) information visualization on exploratory search when given the task of researching a topic and completing a short essay Investigate how users explore topics Explore how users construct search queries

Background Individual Differences Term-net / Concept Map Web Trace The ways in which individuals differ in behavior. The different processes that individuals follow or use. Term-net / Concept Map A graph providing a visualization of terms / concepts and the relationship between them. Nodes (terms / concepts) and Edges (relationships). Web Trace Technique for instrumenting, analyzing, and visualizing user activity on the web.

Research Questions What information seeking and learning strategies do learners use when interacting with conceptual maps to find and integrate information in writing an essay on a given theme? What is the effect of individual differences on information seeking and learning strategies when interacting with conceptual maps to find and integrate information in writing an essay on a given theme?

Hypothesis Expecting to see application of strategies via a careful analysis of web trace data Selection and navigation. Trial-and-error. Indicators expected to include number of search keywords, page viewing times, page content, and the number of resources that participants make use of. Expecting a correlation between Relative level of motivation. Amount of time spent applying strategies, using the search tools, and navigating through the concept map.

Materials Each participant will require: Computer workstation Customized web browser Internet access Access to “custom search portal” tool

Method Laboratory experiment Participants tasked with using a search tool (http://searchapp.net) Goal is to research and write a short 200-300 word essay Topic: “Identify a security technology that is used by people every day. Then, exploring the topics in the term-net, describe how a security exploit might impact someone.”

Experimental Process Participants given overview of the study, and provided with informed consent. Participants provided with anonymous login for the search tool and concept map. Participants complete tutorial guiding them through the search tool and concept map. Participants given achievement goal orientation questionnaire, as well as a pre-test Likert-scale questionnaire to evaluate their existing knowledge of the topic. Participants given topic and associated concept map. Participants provided with the essay question. Participants instructed to conduct research using the concept map, and then write a short 200- 300 word essay response based on their research. Participants asked to complete post-test tool evaluation and perceived usefulness questionnaires.

Measurement Research method requires logging user activity for analysis. Participants provided with a web-based search application. Unique, anonymous Login ID. Based on the Google Custom Search Engine (CSE) technology. Allows us to target specific sets of data for the user to search. Allows for precise logging of user activity.

Experimental Data Anonymous Login ID assigned to the participant Pre-study Achievement Goal Orientation (AGO) Likert-scale answers Pre-study Prior Knowledge Assessment Likert-scale answers Need for Cognition questionnaire Likert-scale answers The essays to the set theme to be prepared by the participant during the experiment Web trace (log) data collected by the web search tool

Data Analysis Post-experimental analysis Statistical data for web traces Cluster analysis for graph (concept map) usage Comparative analysis between graded essay responses and cluster analysis

Selected References Butcher, Kirsten R., and Tamara Sumner. "Self-directed learning and the sensemaking paradox." Human– Computer Interaction 26.1-2 (2011): 123-159. Hadwin, A., Nesbit, J., Jamieson-Noel, D., Code, J., & Winne, P. (2007). Examining trace data to explore self-regulated learning. Metacognition Learning, 2, 107–124. Marchionini, Gary. "Exploratory search: from finding to understanding." Communications of the ACM 49.4 (2006): 41-46. Novak, Joseph. "Concept mapping: A useful tool for science education." Journal of research in science teaching 27.10 (1990): 937-949. Puustinen, Minna, and Jean-François Rouet. "Learning with new technologies: Help seeking and information searching revisited." Computers & Education 53.4 (2009): 1014-1019. Winne, P. (1982). Minimizing the black box problem to enhance the validity of theories about instructional effects. Instructional Science, 11, 13-28. Winne, P., & Perry, N. (2000). "Measuring self-regulated learning”. Full references available on request.