An explicit model for learning to structure and analyze decisions by judges

Legal practitioners and legal scientists need to have knowledge of the general rules that apply in the legal system. This involves both knowledge of the legislation and knowledge of the decisions by judges that function as general rules of law. Law students preparing themselves for the legal profession need to acquire these kinds of knowledge. A student has to have knowledge about where to look for decisions, understand the structure of decisions and learn to determine what makes a decision relevant to the body of applicable rules in the legal system. Legal education primarily aims at acquiring insight in the legal sources, their history and background. This basic knowledge is of great importance; legal problem solving is hardly possible without an understanding of the legal knowledge. To illustrate the use of this knowledge in practice, teachers work through decisions as examples. However, it is difficult, if not impossible, to learn by explanation or by imitation alone. A more effective way to obtain expertise is by actually performing the task, i.e. students should do the exercises, while the teacher provides feedback on their solutions. For effective learning, also the solution process should be monitored and provided with feedback. Furthermore it is desirable for students to be able to ask for help at any time during the process. They should also be able to practice over and over again. An ideal situation would have a teacher available for every student, monitoring the student while practicing and providing support where and whenever necessary. However, this being not practically feasible, the second best option is to offer the student electronic support. CASE (Case Analysis and Structuring Environment) is an environment where a law student can practice with finding decisions, with structuring its text and with analysing the decision in order to be able to determine in what way it adds to the body of applicable rules in the legal system. CASE is developed using a principled and structured design approach. A short description of this approach is followed by an analysis of the learning task, the difficulties law students experience and the remedies proposed on the basis of both the task analysis and the stated difficulties. This is followed by a description of architecture, functionality, platform and implementation of CASE and a description of a session with CASE and future work

Bored with point and click?

Computers have the potential to be exploited as one of the most exciting examples of instructional media. Yet designers often fail to realize this potential. This is, in part, due to the limitations of hardware and software and, in part, due to the lack of good theory developed through conclusive research. Good examples of computer-based learning may owe more to the imaginative flair of the courseware designer than they do to the application of explicit design guidelines and good learning theory. This paper will therefore consider a variety of issues that may be blocking theoretical development and draw conclusions for future courses of action. This starts with a statement of the problem, first by considering the macro and micro issues, and then by looking at a recent call for help in ComputerBased Learning Environment (CBLE) design. Next, the contribution of instructional design theories will be presented together with a way forward for investigating the issues. Finally the implications for future progress are presented

Linguistics and LIS: A Research Agenda

Linguistics and Library and Information Science (LIS) are both interdisciplinary fields that draws from areas such as languages, psychology, sociology, cognitive science, computer science, anthropology, education, and management. The theories and methods of linguistic research can have significant explanatory power for LIS. This article presents a research agenda for LIS that proposes the use of linguistic analysis methods, including discourse analysis, typology, and genre theory

Learning a Pose Lexicon for Semantic Action Recognition

This paper presents a novel method for learning a pose lexicon comprising semantic poses defined by textual instructions and their associated visual poses defined by visual features. The proposed method simultaneously takes two input streams, semantic poses and visual pose candidates, and statistically learns a mapping between them to construct the lexicon. With the learned lexicon, action recognition can be cast as the problem of finding the maximum translation probability of a sequence of semantic poses given a stream of visual pose candidates. Experiments evaluating pre-trained and zero-shot action recognition conducted on MSRC-12 gesture and WorkoutSu-10 exercise datasets were used to verify the efficacy of the proposed method.Comment: Accepted by the 2016 IEEE International Conference on Multimedia and Expo (ICME 2016). 6 pages paper and 4 pages supplementary materia

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

Scripts and scaffolds In Problem-based computer supported collaborative learning environments: fostering participation and transfer

This study investigates collaborative learning of small groups via text-based com-puter-mediated communication. We analyzed how two approaches to pre-structure communication influence participation, individual knowledge transfer, the conver-gence of participation and the convergence of knowledge among learning partners. We varied the factor "scripted cooperation" and the factor "scaffolding" in a 2x2-design. 105 university students of Pedagogy participated. Results show that scrip-ted cooperation was most and scaffolding least beneficial to individual transfer, knowledge convergence and participation in comparison to open discourseDiese Studie befasst sich mit kooperativem Lernen in Kleingruppen über text-basierte computervermittelte Kommunikation. Es wurden zwei Ansätze der Vor-strukturierung von computervermittelter Kommunikation und ihre Auswirkungen auf Partizipation, individuellen Wissenstransfer, die Konvergenz der Partizipation und die Wissenskonvergenz innerhalb einer Lerngruppe untersucht. Dabei wurden die Faktoren "Kooperationsskript" und "Scaffolding" in einem 2x2-Design variiert. 105 Studierende der Pädagogik nahmen teil. Die Ergebnisse zeigen, dass sich das Ko-operationsskript am günstigsten und das Scaffolding am wenigsten günstig auf individuellen Wissenstransfer, Wissenskonvergenz und Partizipation im Vergleich zu einer Kontrollgruppe des 'Offenen Diskurses' ausgewirkt ha

Students' epistemological framing in quantum mechanics problem solving

Students' difficulties in quantum mechanics may be the result of unproductive framing and not a fundamental inability to solve the problems or misconceptions about physics content. We observed groups of students solving quantum mechanics problems in an upper-division physics course. Using the lens of epistemological framing, we investigated four frames in our observational data: algorithmic math, conceptual math, algorithmic physics, and conceptual physics. We discuss the characteristics of each frame as well as causes for transitions between different frames, arguing that productive problem solving may occur in any frame as long as students' transition appropriately between frames. Our work extends epistemological framing theory on how students frame discussions in upper-division physics courses.Comment: Submitted to Physical Review -- Physics Education Researc