Causality is pervasive and ubiquitous. It helps build intellectual understanding, supports deliberations, is involved in planning, in technology, and even in language. It is part of everyday decisions, requiring implications and consequences to be considered in the process of evaluation and judgment. Causality is also a construct of intelligence. Its understanding is critical to cognition and learning. I am working towards understanding how we can help people reason and learn in causal contexts.
Today’s world produces ever increasing amounts of data which, when used properly, can provide insights that were not possible before. While these insights are helpful, the process that leads to them, which often involves statistical analyses, can look so intimidating so much so that many will not even try to use this treasure trove of data available at their fingertips. Moreover, it can be even dangerous to use insights that were improperly derived from the data. I, for one, believe that quantitative analysis and statistics should be approachable, for which reason I began developing a perpetually updated Quantitative research by example book, available for free to everyone. I decided to develop it from the point of view that while deep understanding of how a statistical test works and what it does requires deep understanding of mathematics and probabilities, its use to understand a quantifiable phenomena does not have to be overwhelming.
Concept maps are phenomenal tools for assessing structural knowledge of a domain or around a concept. Nevertheless, when it comes to use them for assessment, the come with a significant disadvantage because the best way to assess them is by hand. Today's technology offers us affordances that were limited in the past, both in constructing concept maps and analyzing them. This line of research follows two paths. One is the design and development of a web-based concept-map design application; the k-Map application listed above. The second one is to study its usefulness in assessing structural knowledge.
People are reluctant to accept technology even though important financial and human resources are spent on technology and technology training. Existing research is descriptive and predictive, but lacks the prescriptive component. Therefore, starting from the concept of technology acceptance in organizations, over several iterations I developed the theoretical support as well as the research methodology based on the idea that the acceptance issues can be alleviated upfront, during the pre-implementation training sessions, by contextualizing the training to the specifics of the trainees in each particular training session.