What Can Be Learned from Computer Modeling? Comparing Expository and Modeling Approaches to Teaching Dynamic Systems Behavior

Computer modeling has been widely promoted as a means to attain higher order learning outcomes. Substantiating these benefits, however, has been problematic due to a lack of proper assessment tools. In this study, we compared computer modeling with expository instruction, using a tailored assessment designed to reveal the benefits of either mode of instruction. The assessment addresses proficiency in declarative knowledge, application, construction, and evaluation. The subscales differentiate between simple and complex structure. The learning task concerns the dynamics of global warming. We found that, for complex tasks, the modeling group outperformed the expository group on declarative knowledge and on evaluating complex models and data. No differences were found with regard to the application of knowledge or the creation of models. These results confirmed that modeling and direct instruction lead to qualitatively different learning outcomes, and that these two modes of instruction cannot be compared on a single “effectiveness measure”

Network Psychometrics

This chapter provides a general introduction of network modeling in psychometrics. The chapter starts with an introduction to the statistical model formulation of pairwise Markov random fields (PMRF), followed by an introduction of the PMRF suitable for binary data: the Ising model. The Ising model is a model used in ferromagnetism to explain phase transitions in a field of particles. Following the description of the Ising model in statistical physics, the chapter continues to show that the Ising model is closely related to models used in psychometrics. The Ising model can be shown to be equivalent to certain kinds of logistic regression models, loglinear models and multi-dimensional item response theory (MIRT) models. The equivalence between the Ising model and the MIRT model puts standard psychometrics in a new light and leads to a strikingly different interpretation of well-known latent variable models. The chapter gives an overview of methods that can be used to estimate the Ising model, and concludes with a discussion on the interpretation of latent variables given the equivalence between the Ising model and MIRT.Comment: In Irwing, P., Hughes, D., and Booth, T. (2018). The Wiley Handbook of Psychometric Testing, 2 Volume Set: A Multidisciplinary Reference on Survey, Scale and Test Development. New York: Wile

Computationeel denken in de wiskundeles: Hoe krijg je dat voor elkaar?

Computationeel denken staat in grote belangstelling in het onderwijs. Ook in de wiskundeles zijn er mogelijkheden om de principes van de informatica toe te passen in het oplossen van wiskundige problemen. In deze presentatie vertel ik over ons onderzoek 'Computationeel denken en wiskundig denken: digitale geletterdheid in wiskundecurricula' waarin we Excel en Geogebra gebruiken, en komt u meer te weten over de onderzoeksontwikkelingen op het gebied van dit grensvlak. Verder laat ik een voorbeeld zien van het gebruik van augmented reality en een 'visuele' programmeertaal Blockly. Het tweede deel van de presentatie heeft meer een werkgroepkarakter. Door middel van vragen en voorbeelden krijgt u de gelegenheid om zelf na te denken over hoe u computationeel denken in uw eigen les zou kunnen integreren

The assessment of learning outcomes of computer modeling in secondary science education

The computer modeling of dynamic systems is a topic that aligns well with the current trend in secondary science education to actively involve students in their knowledge construction, give room for inquiry, and offer realistic tasks. In this dissertation the specific learning outcomes that can be expected from computer modeling are investigated, as well as how they can be measured. The reasoning processes of modeling are explored and systematized as what is called the ‘ACE framework’ that describes modeling knowledge in three dimensions: 'type of reasoning', 'complexity', and 'domain-specificity'. The dimension of ‘types of reasoning’ includes applying (A), creating (C), and evaluating (E). The dimension of complexity distinguishes between reasoning with simple and complex situations. The dimension of domain-specificity describes the extent to which reasoning is dependent on the domain and distinguishes between domain-specific and domain-general. The framework was used to develop the 'ACE test' for the domain of the energy of the Earth. In three studies the ACE test is validated and used to compare different modes of instruction: modeling-based, simulation-based and an expository mode of instruction. \ud A general pattern that arose from the comparative studies is the difference in performance between the groups with respect to simple and complex items and processes. Overall, the students in the modeling-based mode of instruction performed better on the complex items. The modeling processes are progressively more demanding with regard to the types of reasoning involved, from the straightforward reproducing of conceptual knowledge to the higher-order processes of applying, creating, and evaluating. The findings indicate a benefit for the learners in the expository and simulation-based instruction on the simplest process of reproducing conceptual knowledge. The modelers benefited progressively more than the learners in the simulation-based instruction on the complex items and the more demanding processes of applying and creating. However, no differences were found for the process of evaluating between learners in the modeling and the simulation-based mode of instruction

Inflammatory Phenotype of Depression Symptom Structure: A Network Perspective

There has been increasing interest in classifying inflammatory phenotypes of depression. Most investigations into inflammatory phenotypes solely have tested whether increased inflammation is associated with increased depression. This study expanded the definition of phenotype to include the structure of depression as a function of inflammation. Network models of depression symptoms were estimated in a sample of 4,537 adults from the 2015-2016 National Health and Nutrition Examination Survey (NHANES). Analyses included comparisons of networks between those with elevated (C-reactive protein (CRP) values ≥ 3.0 mg/L) and non-elevated CRP as well as moderated network models with CRP moderating the associations between depression symptoms. Differences emerged at three levels of analysis (global, symptom-specific, symptom—symptom associations). Specifically, the elevated CRP group had greater global connectivity. Further, difficulty concentrating had higher expected influence (concordance with other symptoms) in the elevated CRP group. Finally, 14 out of 38 symptom—symptom associations were moderated by CRP. This study provides consistent evidence that the structure of depression symptoms varies as a function of CRP levels.Temple University. College of Liberal ArtsPsychology and Neuroscienc