Reflective structured dialogue: A conversation with 2018 American Academy of Religion excellence in teaching award winner Jill DeTemple

This conversation between the 2018 American Academy of Religion Excellence in Teaching Award winner Jill DeTemple and the editors of Teaching Theology and Religion continues an occasional series of interviews that has previously featured Jonathan Z. Smith, Stephen Prothero, Mary Pierce Brosmer, Mary Elizabeth Mullino Moore, and two previous AAR Teaching Award Winners, Joanne Maguire and Lynn Neal. After initial discussion about teaching the intro course we launch into a long discussion of “Reflective Structured Dialogue” – an effective teaching technique for staging contentious conversations, building trust and understanding, and a dialogic culture of curiosity. 1 1 The authors would like to dedicate this article to the memory of Ruel W. Tyson, Jr., a champion of collaborative scholarship and intellectual community. The research referenced in this article was supported by a subaward agreement from the University of Connecticut with funds provided by Grant No. 58942 from the John Templeton Foundation. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of UCONN or the John Templeton Foundation.
     

Differentiating the Effects of Informational and Interpersonal Justice in Co‐Worker Interactions for Task Accomplishment

To extend the research on interactions‐based justice and integrate it with the research on trustworthiness, the present article examines the role of informational and interpersonal justice in co‐worker interactions for task accomplishment. Based on social exchange theory and fairness heuristic theory, the two justice dimensions would trigger different types of responses based on the principle of reciprocity and how they manage different types of uncertainties in a work relationship. As a result, they lead to different outcomes in the context of two co‐workers having a disagreement about work. It was hypothesised that informational justice is related to acceptance of the co‐worker's view primarily through perception of ability‐based trustworthiness, whereas interpersonal justice is related to satisfaction with the co‐worker primarily through perception of benevolence‐based trustworthiness. A survey of employees and a simulation study showed that the effect of informational justice on acceptance of the co‐worker's view is mediated more by ability‐based trustworthiness than by benevolence‐based trustworthiness, whereas the reverse is true for the mediation of the effect of interpersonal justice on satisfaction with the co‐worker. Theoretical implications are discussed concerning the differentiation of the two highly correlated justice dimensions, together with practical implications regarding their relative usefulness.     

Highlights as an Early Predictor of Student Comprehension and Interests

When engaging with a textbook, students are inclined to highlight key content. Although students believe that highlighting and subsequent review of the highlights will further their educational goals, the psychological literature provides little evidence of benefits. Nonetheless, a student’s choice of text for highlighting may serve as a window into her mental state—her level of comprehension, grasp of the key ideas, reading goals, and so on. We explore this hypothesis via an experiment in which 400 participants read three sections from a college-level biology text, briefly reviewed the text, and then took a quiz on the material. During initial reading, participants were able to highlight words, phrases, and sentences, and these highlights were displayed along with the complete text during the subsequent review. Consistent with past research, the amount of highlighted material is unrelated to quiz performance. Nonetheless, highlighting patterns may allow us to infer reader comprehension and interests. Using multiple representations of the highlighting patterns, we built probabilistic models to predict quiz performance and matrix factorization models to predict what content would be highlighted in one passage from highlights in other passages. We find that quiz score prediction accuracy reliably improves with the inclusion of highlighting data (by about 1%–2%), both for held-out students and for held-out student questions (i.e., questions selected randomly for each student), but not for held-out questions. Furthermore, an individual’s highlighting pattern is informative of what she highlights elsewhere. Our long-term goal is to design digital textbooks that serve not only as conduits of information into the reader’s mind but also allow us to draw inferences about the reader at a point where interventions may increase the effectiveness of the material.