Each student saves his/her map and matrix in an Excel file. When all students files are collected into a file directory with the jMAP software, the instructor and/or students can import and aggregate all student's diagrams into a standardized map template (the template can be the expert’s map or any individual student’s map). Using this approach, each students' diagram can be superimposed over the diagrams of other individuals (including his/her own diagram from an earlier time), the diagram of the collective group, or the target/expert diagram (with dark-green colored arrows identifying shared links with same impact values, ight-green links identifying shared links bu with different assigned impact values, and gray colored arrows revealing missing or not shared links). As a result, visual comparisons can be performed between: a) student A’s diagram produced at time 1 vs. time 2 to assess each student's learning trajectory; b) student A’s vs. the expert diagram; c) a group diagram (produced by aggregating all diagram across all students) vs. an expert model; and most of all d) an individual vs. group diagram to assess convergence to a shared model/understanding between group members. Users can rapidly toggle between diagrams produced over different times to animate and visually assess how diagrams change over time and the extent to which the changes are converging toward an expert or collective group diagram.

Identifying Action Sequences that Produce High Quality Maps. Here are four empirical studies that have been conducted using jMAP to log & mine each action students perform as they are constructing their maps. By applying sequential analysis to this data and comparing the resulting transitional state diagrams, several empirical studies identified specific action sequences/patterns performed by students that produce high vs low quality maps.

Studying Change Over Time. Additional jMap tools enable users to compile raw scores to: a) compare quantitative measures (e.g. test rate of change in number of matching links); and b) sequentially analyze and identify patterns in the way causal link strengths change over time using both jMap and DAT software . For example, Figure 2 shows that the absence of evidence to support, justify, and illustrate a causal relationship between two nodes make learners much more likely to remove or reduce the causal link strength values in subsequent revisions to their causal diagrams (Jeong 2008). When evidence is present to support or validate a causal relationship, the causal link strengths are more likely to remain the same or increase in value.

Psychological Reasons & Rational for Constructing & Comparing Maps in Groups

• • When a learner constructs a map, the learner is better able to identify and articulate the semantic/causal relationships between concepts/factors presented in text. The act of translating information from a text format to a node-link format requires learners to process meaning more deeply than they normally do when reading text or listening to a lecture.

  • Long-term retention of the information is increased as the learner encodes the information in both verbal and visual representations.

  • A learner can achieve deeper understanding when they: a) start by working individually to construct their maps to capture their prior knowledge, understanding, and individual viewpoints; b) share and compare their maps with other students to identify and discuss differences in knowledge and points of view; and c) work individually to revise their own maps to integrate and assimilate new information into their prior knowledge/understanding.

  • Maps facilitate cooperative learning processes because maps: a) makes economical use of text by writing concepts in letters that are large enough to be viewed by a small group; b) can be easily extended with less need for re-organization and erasure than lists and outlines; c) are more amenable to concurrent editing because the semantic dependencies between concepts are more explicitly represented; and d) do not require detailed writing activities that can take time away from substantive discussion.

Evidence for Constructing Maps Using Mix of Individual and Group Work

• • Nesbit & Adesope (2006) review and meta-analysis of the empirical research revealed that maps were most effective when learners performed a mix of both individual and cooperative work when constructing their maps (ES = +.955, k = 10) as opposed to individually constructing a map (ES = +.119, k = 5) versus individually studying a given map (ES = .402, k =32) versus cooperatively studying a given map (ES = +.192, k = 8). In other words, a learner that constructs his/her map using a mix of both individual and cooperative work can achieve: a) more than 500% gain in learning in comparison having the student construct his/her map individual; and b) more than 200% gain in learning compared to having the student individually study a map presented in a class reading or class lecture.

Potential Applications

• • Use jMAP to solve complex ill-structured problems. Construct causal maps to coordinate the collaborative process of conducting an inter-relational digraph analysis to identify and rank order factors or events that exert the greatest influence (factors that are potentially root causes) and identify which factors should be addressed and given the greatest priority. See group exercise illustrating the use of the digraph method.

  • Use jMAP to assess students' concept maps (individually or collectively) and instruct students to label the semantic links by commenting on each link. To comment on a link, select a link and type CTRL-r.

  • Use jMAP to assess students' goal and subordinate skill analysis (individually or collectively) in your instructional design course.

  • Instructional strategies for using causal maps as a teaching tool

  • Assess students' argument diagrams & analysis.

  • Facilitates systems thinking. Why is systems thinking important? "Have you ever wondered why the best of intentions so often don't get implemented, why there are certain problems that we seem to solve over and over, and still they keep coming back, why continuing to do the same thing at times produces different results, or no results at all, or why often solving problems is a lot like playing whack-a-mole in that every time you solve a problem a new one pops up somewhere else?...More often than not organizations invest huge amounts of effort into the development of approaches for dealing with situations though typically those approaches either never get implemented or when they are implemented fail to be implemented in a manner that actually produces the intended or expected results. Systemic Strategy Development provides an approach for the development of a strategy for dealing with a situation which is highly likely to be implemented, and when implemented it will address the situation of interest without creating myriad of unexpected consequences which tend to make the situation worse or create new problems one must deal with later." (Systemswiki.org)

Research Questions Addressed with jMAP

• 1. Which processes help to produce more accurate causal maps? Working forwards vs.working backwards to/from the final outcome; depth first vs. breadth first vs. combination of depth & breadth? See Jeong 2014

  2. To what extent are the causal maps a measure of the user's understanding of the problem vs. the user's knowledge/skills in constructing causal maps?

  3. What are the processes used by novices? To what extent does the novice's level of content knowledge affect the processes used by the novice?

  4. What processes are used by content experts? To what extent do these processes mirror commonly used problem solving approaches like "The five whys" and "Fishbone technique"?

  5. How do the processes used by experts differ from the processes used by novices?

  6. To what extent does the content or subject matter affect the causal mapping process? If there are differences in process between subject matter, what are the similar and fundamental processes shared across subject matter? [related study]

  7. When examining all or a subset of links collectively, what patterns exist in the way the links change over time?

  8. How do certain variables and discourse processes affect the way causal maps change over time, particularly changes that converge toward that of the collective group and/or the expert model?

  9. To what extent do observed changes in the diagrams of individuals or collective group converge towards the expert's diagram ?

  10. To what extent do observed changes in the diagrams of individual learners converge towards the diagram representing the collective group?

  11. How do particular events/conditions affect the learning trajectory or rate at which the diagrams of learners converge toward the expert diagram?

Similar Software Applications

• • Cognizer by Nakayama and Liao. Also, see article by Gail P Clarkson, G. P. H. (2005). "Introducing Cognizer(TM): A Comprehensive Computer Package for the Elicitation and Analysis of Cause Maps." Organizational Research Methods, 18(3): 317-344. Abstract:Managerial and organizational cognition (MOC) researchers have issued a number of calls over the years for large-scale studies involving the mass application of causal mapping techniques. A number of important advances in the development of procedures for the systematic elicitation and comparison of cause maps mean that such work has been technically feasible for some time. However, due to a dearth of suitable supporting computer software, very few researchers to date have responded to this key challenge, vital to the longer-term viability of the MOC field. Building on innovations by Langfield-Smith and Wirth and Markoczy and Goldberg, the authors report on the development of Cognizer, a comprehensive computer package designed to meet the requirements of researchers looking to elicit and compare large numbers of maps on a longitudinal or cross-sectional basis. The principal features of Cognizer are illustrated using a cross-sectional data set comprising 200 cause maps from five organizations.

  • Causality Lab (Carnegie Mellon)

  • Causal Mapper (Berkeley)

• Insight Maker

  • Betty's Brain [video]

• Other causal mapping software

References

• • Nesbit, J., & Adesope, O. (2006). Learning with concept and knowledge maps: A meta-analysis. Review of Educational Research, 76(3): 413-448.

Future Updates & Fixes

• At this time, the size of the halo surrounding each node represents the frequency in which the node/action was observed relative to the frequencies observed in the other nodes/actions. When we place two state diagrams side by side, the size of the halo on one node in one diagram cannot be compared to the size of the corresponding node in the other state diagram. Therefore, future versions will enable the user to place two state diagrams side by side and generate the halos with respect to the observed frequencies across all nodes in both state diagrams.