How to improve argumentation comprehension in university students: experimental test of a training approach

Instructional Science - The ability to comprehend informal arguments is essential for scientific literacy but students often lack structural knowledge about these arguments, especially when the...     

Identifying paths to audience success of media products: the media decision-makers’ perspective

Research on media success factors is a fragmented field. Definitions, measures, and methods vary, and findings are often inconsistent. In an attempt to fill this perceived research gap, we distilled generic success factors of media products from the literature. Guided by theory and empirical findings, these factors were aggregated to complex concepts, building blocks of success that we further investigated in an exploratory qualitative study. We found that the building blocks are applicable to all types of media, independent of seriality and content types of media products. Subsequently the research question of this article is: Which building blocks of success are most important for media products? To answer this question, we conducted an online survey of 255 media professionals in print, audio-visual, and online media in Austria, Germany and Switzerland. To analyze our data, we deployed qualitative comparative analysis, a method based on set theory that is suitable to investigate complex causality. We conclude that four building blocks are necessary for success: “good” distribution, environmental orientation, form/design, and human resources are preconditions for achieving success in terms of audience market share. In addition, three patterns emerge in the sufficient paths (combinations of building blocks) to success. Which route to success a media product shows can be related to the width of its topical scope and the corresponding projected audience size.     

Using Networks to Visualize and Analyze Process Data for Educational Assessment

New technology enables interactive and adaptive scenario‐based tasks (SBTs) to be adopted in educational measurement. At the same time, it is a challenging problem to build appropriate psychometric models to analyze data collected from these tasks, due to the complexity of the data. This study focuses on process data collected from SBTs. We explore the potential of using concepts and methods from social network analysis to represent and analyze process data. Empirical data were collected from the assessment of Technology and Engineering Literacy, conducted as part of the National Assessment of Educational Progress. For the activity sequences in the process data, we created a transition network using weighted directed networks, with nodes representing actions and directed links connecting two actions only if the first action is followed by the second action in the sequence. This study shows how visualization of the transition networks represents process data and provides insights for item design. This study also explores how network measures are related to existing scoring rubrics and how detailed network measures can be used to make intergroup comparisons.