Perceiving versus inferring movements to understand dynamic events: The influence of content complexity

ObjectivesWhat type of visual presentation is best in helping learners to understand the functioning of a dynamic system and under what conditions? This study investigated the effect of content complexity on perceived cognitive load and performance resulting from studying depicted movements of team play either in an explicit manner (animation) or via arrow symbols (static diagram).DesignA 2 (treatment: diagram vs. animation) × 2 (content complexity: low vs. high) between subjects design was adopted in the experiment.MethodsForty-eight university students were randomly assigned to the four study conditions and required to perform a reconstruction test and rate their perceived cognitive load following the study phase.ResultsData analyses revealed that for low-complexity content, participants exposed to the animation treatment learned more efficiently – based on the combination of learning and cognitive load scores – than those exposed to the diagram treatment. On the other hand, for high-complexity content, participants exposed to the diagram treatment learned more efficiently than those exposed to the animation treatment.ConclusionThe findings stress the importance of considering the task complexity factor when designing and presenting instructional materials to learners.     

Human dignity and human rights in the philosophy of absolute idealism

The tides of change constantly surface new currents in the world of business. No longer is it sufficient to seek the static positional advantage offered by classical Porterian analysis. This article explores the emerging direction of business strategy as expressed by the concept of evolutionary advantage. It examines first the major forms of business knowledge over the last century, then considers mainstream frameworks for strategic analysis, and offers, as a compelling alternative, the emerging notions of evolutionary development and evolutionary learning. Through application of Evolutionary Systems Design, it is shown how an organization may cultivate strategies for sustainable success.     

王夫之易学中的实有思想与清初务实学风

王夫之治《易》，务实求真，主张非无而有，非虚而实．非妄而真，其批判矛头直指佛老，提出了天地自然皆为实有的思想，从客观实际出发，说明天地自然的本性。王夫之易学中的实有思想与清初务实学风有关，受着明清之际反对虚理，崇尚务实思潮的影响。从哲理或思辨意义上说，他对实有思想研讨之湛深，创获之丰富．超拔于同时代人。     

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.     

An effective metacognitive strategy: learning by doing and explaining with a computer‐based Cognitive Tutor

Recent studies have shown that self‐explanation is an effective metacognitive strategy, but how can it be leveraged to improve students' learning in actual classrooms? How do instructional treatments that emphasizes self‐explanation affect students' learning, as compared to other instructional treatments? We investigated whether self‐explanation can be scaffolded effectively in a classroom environment using a Cognitive Tutor, which is intelligent instructional software that supports guided learning by doing. In two classroom experiments, we found that students who explained their steps during problem‐solving practice with a Cognitive Tutor learned with greater understanding compared to students who did not explain steps. The explainers better explained their solutions steps and were more successful on transfer problems. We interpret these results as follows: By engaging in explanation, students acquired better‐integrated visual and verbal declarative knowledge and acquired less shallow procedural knowledge. The research demonstrates that the benefits of self‐explanation can be achieved in a relatively simple computer‐based approach that scales well for classroom use.