This is the second post in a series on a recent paper entitled “Age-related nuances in knowledge assessment” that we wrote with Luc Watrin and Oliver Wilhelm. The first post dealt with the way how we conceptualize the organization of knowledge in a hierarchy in a multidimensional knowledge space. The second post reflects on the way we measure or model knowledge. In textbooks knowledge assessments have a special standing, because they can be modeled both from a reflective and a formative perspective.
Tagged "machine learning"
We published a new paper entitled “Age-related nuances in knowledge assessment” in Intelligence. I really like this paper because it deals with on the way we assess, model, and understand knowledge. And, btw, it employs machine learning methods. Thus, both in terms of content and methodology it hopefully sets a stage for future research avenues that are promising to follow up on. I would like to cover some of the key findings in a series of blog posts.
Intelligence Research Our understanding of intelligence has been — and still is — significantly influenced by the development and application of new testing procedures as well as novel computational and statistical methods. In science, methodological developments typically follow new theoretical ideas. In intelligence research, however, great breakthroughs often followed the reverse order. For instance, the once-novel factor analytic tools preceded and facilitated new theoretical ideas such as the theory of multiple group factors of intelligence.