Understanding when students are active‐in‐thinking through modeling‐in‐context

• Published in: British journal of educational technology 2019-09, Vol.50 (5), p.2346-2364
• Main Authors: Swiecki, Zachari, Ruis, Andrew R., Gautam, Dipesh, Rus, Vasile, Williamson Shaffer, David
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Authoring; Automation; Behavioral Objectives; Cognitive Processes; Comprehension; Computer & video games; Computer simulation; Critical components; Curricula; Customization; Education; Educational Technology; Machine learning; Modelling; Norms; Pedagogy; Scoring Rubrics; Students; Teachers; Teaching Methods; Technology Uses in Education
• ISSN: 0007-1013; 1467-8535
• DOI: 10.1111/bjet.12869

Learning‐in‐action depends on interactions with learning content, peers and real world problems. However, effective learning‐in‐action also depends on the extent to which students are active‐in‐thinking, making meaning of their learning experience. A critical component of any technology to support active thinking is the ability to ascertain whether (or to what extent) students have succeeded in internalizing the disciplinary strategies, norms of thinking, discourse practices and habits of mind that characterize deep understanding in a domain. This presents what we call a dilemma of modeling‐in‐context: teachers routinely analyze this kind of thinking for small numbers of students in activities they create or customize for the needs of their students; however, doing so at scale and in real‐time requires some automated processes for modeling student work. Current techniques for developing models that reflect specific pedagogical activities and learning objectives that a teacher might create require either more expertise or more time than teachers have. In this paper, we examine a theoretical approach to addressing the problem of modeling active thinking in its pedagogical context that uses teacher‐created rubrics to generate models of student work. The results of this examination show how appropriately constructed learning technologies can enable teachers to develop custom automated rubrics for modeling active thinking and meaning‐making from the records of students' dialogic work. Practitioner Notes What is already known about this topic Many immersive educational technologies, such as digital games and simulations, enable students to take consequential action in a realistic context and to interact with peers, mentors and pedagogical agents. Such technologies help students to be active‐in‐thinking: engaging deeply with, reflecting on and otherwise making meaning of their learning experience. There are now many immersive educational technologies with integrated authoring tools that enable teachers to customize the learning experience with relative ease, reducing barriers to adoption and improving student learning. Educational technologies that support learning‐in‐action typically contain student models that operate in real‐time to control the behavior of pedagogical agents, deliver just‐in‐time interventions, select an appropriate content or otherwise measure and promote active thinking, but these student models may not work appropriately if teachers customi