English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Predicting long-term outcomes of interventions is necessary for educationaland social policy-making processes that might widely influence our society forthe long-term. However, performing such predictions based on data fromlarge-scale experiments might be challenging due to the lack of time andresources. In order to address this issue, computer simulations based onEvolutionary Causal Matrices and Markov Chain can be used to predict long-termoutcomes with relatively small-scale lab data. In this paper, we introducePython classes implementing a computer simulation model and presented somepilots implementations demonstrating how the model can be utilized forpredicting outcomes of diverse interventions. We also introduce theclass-structured simulation module both with real experimental data and withhypothetical data formulated based on social psychological theories. Classesdeveloped and tested in the present study provide researchers and practitionerswith a feasible and practical method to simulate intervention outcomesprospectively.

Simulating outcomes of interventions using a multipurpose simulation program based on the Evolutionary Causal Matrices and Markov Chain