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Recent years have seen a surge in the use of intelligent computer-supported collaborative learning (CSCL) tools for improving student learning in traditional classrooms. However, adopting such a CSCL tool in a classroom still requires the teacher to develop (or decide on which to adopt) the CSCL tool and the CSCL script, design the relevant pedagogical aspects (i.e., the learning objectives, assessment method, etc.) to overcome the associated challenges (e.g., free riding, student assessment, forming student groups that improve student learning, etc). We have used a multiagent-based system to develop a CSCL application and multiagent-frameworks to form student groups that improve student collaborative learning. In this paper, we describe the contexts of our three generations of CSCL applications (i.e., I-MINDS and ClassroomWiki) and provide a set of lessons learned from our deployments in terms of the script, tool, and pedagogical aspects of using CSCL. We believe that our lessons would allow 1) the instructors and students to use intelligent CSCL applications more effectively and efficiently, and help to improve the design of such systems, and 2) the researchers to gain additional insights into the impact of collaborative learning theories when they are applied to real-world classrooms. (Contains 5 tables and 3 figures.)

This paper describes the Reinventing Computer Science Curriculum Project at the University of Nebraska-Lincoln. Motivated by rapid and significant changes in the information technology and computing areas, high diversity in student aptitudes, and high dropout rates, the project designed and implemented an integrated instructional/research framework. The framework is based around 10 general design strategies that incorporated administrative, instructional, and research principles. The framework consists of a placement examination, three suites of structured laboratory assignments, multimedia learning objects, and educational evaluation and research designs. The results of implementing the framework in our introductory courses are encouraging and insightful. While validating some of our designs, our research also identified areas for further development and research. (Contains 2 figures and 4 tables.)

An introductory CS1 course presents problems for educators and students due to students' diverse background in programming knowledge and exposure. Students who enroll in CS1 also have different expectations and motivations. Prompted by the curricular guidelines for undergraduate programmes in computer science released in 2001 by the ACM/IEEE, and driven by a departmental project to reinvent the undergraduate computer science and computer engineering curricula at the University of Nebraska-Lincoln, we are currently implementing a series of changes which will improve our introductory courses. One key component of our project is an online placement examination tied to the cognitive domain that assesses student knowledge and intellectual skills. Our placement test is also integrated into a comprehensive educational research design containing a pre- and post-test framework for assessing student learning and providing valuable feedback for needed instructional revisions. In this paper, we focus on the design and implementation of our placement exam and present an analysis of the data collected to date. (Contains 5 tables.)

A framework integrating case-based reasoning (CBR) and meta-learning is proposed in this paper as the underlying methodology enabling self-improving intelligent tutoring systems (ITSs). Pedagogical strategies are stored in cases, each dictating, given a specific situation, which tutoring action to make next. Reinforcement learning is used to improve various aspects of the CBR module – cases are learned and retrieval and adaptation are improved, thus modifying the pedagogical strategies based on empirical feedback on each tutoring session. To minimize canceling out effects due to the multiple strategies used for meta-learning – for example, the learning result of one strategy undoes or reduces the impact of the learning result of another strategy, a principled design that is both cautious and prioritized is put in place. An ITS application, called Intelligent Learning Material Delivery Agent (ILMDA), has been implemented, powered by this framework, on introductory computer science topics, and deployed at the Computer Science and Engineering Department of the University of Nebraska. Studies show the feasibility of such a framework and impact analyses are reported on pedagogical strategies and outcomes. [ABSTRACT FROM AUTHOR]