Stretching the limits in help-seeking research

This special section focuses on help seeking in a wide range of learning environments, from classrooms to online forums. Previous research has rather restrictively focused on the identification of personal characteristics that predict whether or not learners seek help under certain conditions. However, help-seeking research has begun to broaden these self-imposed limitations. The papers in this special section represent good examples of this development. Indeed, help seeking in the presented papers is explored through complementary theoretical lenses (e.g., linguistic, instructional), using a wide scope of methodologies (e.g., teacher reports, log files), and in a manner which embraces the support of innovative technologies (e.g., cognitive tutors, web-based environments)

Adaptive Rückmeldungen im intelligenten Tutorensystem LARGO

The Intelligent Tutoring System LARGO is designed to help law students learn argumentation skills. The approach implemented in LARGO uses transcripts of oral arguments as learning resources: Students annotate them and create graphical representations of the argument flow. The system encourages students to reflect upon arguments proposed by the attorneys and helps students detect possible weaknesses in their analysis of the dispute. Technically, graph grammar and collaborative filtering algorithms are employed to detect these weaknesses. This article describes how “usage contexts” are determined and used to create adaptive feedback in LARGO. On the basis of a controlled study with the system that took place with law students at the University of Pittsburgh, we discuss to what extent the automatically calculated usage contexts can predict student’s learning gains

Behavior Effect of Hint Selection Penalties and Availability in an Intelligent Tutoring System

Proceedings of: Tenth International Confererence on Intelligent Tutoring Systems: Bridges to Learning (ITS 2010). Pittsburg, USA, June 14-18, 2010.his paper presents empirical results about the behavior effect of two different hinting strategies applied on exercises within an ITS: having some penalty on the scoring for viewing hints or not having any effect on the scoring; and hints directly available or only available as a result to an incorrect attempt. We analyze the students' behavior differences when these hinting techniques changed, taking into account the type and difficulty of the presented exercises.Work partially funded by the Learn3 project TIN2008-05163/TSI within the Spanish “Plan Nacional de I+D+I”, and the Madrid regional community project eMadrid S2009/TIC-1650.Publicad

Peer assessment as collaborative learning

Peer assessment is an important component of a more participatory culture of learning. The articles collected in this special issue constitute a representative kaleidoscope of current research on peer assessment. In this commentary, we argue that research on peer assessment is currently in a stage of adolescence, grappling with the developmental tasks of identity formation and affiliation. Identity formation may be achieved by efforts towards a shared terminology and joint theory building, whereas affiliation may be reached by a more systematic consideration of research in related fields. To reach identity formation and affiliation, preliminary ideas for a cognitively toned, process-related model of peer assessment and links to related research fields, especially to research on collaborative learning, are presented

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

Using a Well-Structured Model to Teach in an Ill-Structured Domain

Our goal is to develop a tutoring system, called CATO, that teaches law students skills of making arguments with cases. CATO's domain model provides a plausible account of legal arguments with cases, but is limited in that it does not repre?sent certain background knowledge. It is important, however, that students leam to apply and integrate this background knowledge when making arguments with cases. Given that modeling this background knowledge is difficult in an ill?stiuctured domain like legal reasoning, it is worth exploring how effectively one can teach with a model that represents ar?gument structure but relatively little background knowledge. The CATO instructional envirormient, comprising a case da?tabase and retrieval tools, enables students to apply the CATO model to a specific problem. In a formative evaluation study with 17 beginning law students, we compared instruction with the CATO environment, under the guidance of a human tutor, against more traditional classroom instruction not based on the CATO model. W e found that human-led instruction with CATO is as good as, but not better than, classroom instruction. How?ever, answers generated by the CATO program received higher grades than the students' answers, suggesting that the model can potentially be employed to teach even more effectively. Examples drawn fitom protocols show that students were able to use the CATO model flexibly and integrate background knowledge appropriately, at least when guided by a human tu?tor

Towards the Prediction of User Actions on Exercises with Hints Based on Survey Results

Proceedings of: 6th European Conference of Technology Enhanced Learning, EC-TEL 2011, Palermo, Italy, September 20-23, 2011.The actions a user performs on exercises depending on the different hinting techniques applied, can be used to adapt future exercises. In this paper, we propose a survey for users in order to know their different actions depending on different conditions. The analysis of preliminary results for some questions of the model shows that there is a correlation between some survey questions and the real student actions, but there is a case in which there is not such correlation. For the cases where that correlation exists, this correlation leads to think that some prediction of users actions based on survey results is possible.Work partially funded by the Learn3 project TIN2008-05163/TSI within the Spanish “Plan Nacional de I+D+I”, and the Madrid regional community project eMadrid S2009/TIC-1650

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

When Less is More: Students' Use of Diagrams and their Perception of Diagram Use in an AI Tutor for Algebra Learning

It is critical to understand how students' monitoring activities are related to their actions during learning. In particular, studies have not fully explored how students' spontaneous use of visual representations relate to their perception of its usefulness and their learning outcomes, especially in interactive learning environments. This study, using a math intelligent tutoring system, examines the relations between students' perceptions of the usefulness of using diagrammatic scaffolding and their actual patterns of spontaneous diagram use in for secondary-school algebra. Results show that students who evaluated diagrams as useful used diagrams more frequently but showed less learning gains, compared to those who evaluated diagrams as not useful and did not use diagrams frequently. We discuss implications of this finding by connecting with prior work that focuses on drawing as diagram use. This study shows the importance of understanding how spontaneous use of diagrams might or might not help student learning