Spanning seven orders of magnitude: a challenge for

Abstract. In this paper, we explore the problem of selecting appropriate interventions for students based on an analysis of their interactions with a tutoring system. In the context of the WHY2 conceptual physics tutoring system, we describe CarmelTC, a hybrid symbolic/statistical approach for analysing conceptual physics explanations in order to determine which Knowledge Construction Dialogues (KCDs) students need for the purpose of encouraging them to include important points that are missing. We briefly describe our tutoring approach. We then present a model that demonstrates a general problem with selecting interventions based on an analysis of student performance in circumstances where there is uncertainty with the interpretation, such as with speech or text based natural language input, complex and error prone mathematical or other formal language input, graphical input (i.e., diagrams, etc.), or gestures. In particular, when student performance completeness is high, intervention selection accuracy is more sensitive to analysis accuracy, and increasingly so as performance completeness increases. In light of this model, we have evaluated our CarmelTC approach and have demonstrated that it performs favourably in comparison with the widely used LSA approach, a Naive Bayes approach, and finally a purely symbolic approach.

Although researchers have begun to explicitly support enduser programmers ’ debugging by providing information to help them find bugs, there is little research addressing the proper mechanism to alert the user to this information. The choice of alerting mechanism can be important, because as previous research has shown, different interruption styles have different potential advantages and disadvantages. To explore impacts of interruptions in the end-user debugging domain, this paper describes an empirical comparison of two interruption styles that have been used to alert end-user programmers to debugging information. Our results show that negotiated-style interruptions were superior to immediate-style interruptions in several issues of importance to end-user debugging, and further suggest that a reason for this superiority may be that immediate-style interruptions encourage different debugging strategies.

This article explores 2 important aspects of metacognition: (a) how students monitor their ongoing performance to detect and correct errors and (b) how students reflect on those errors to learn from them. Although many instructional theories have advocated providing students with immediate feedback on errors, some researchers have argued that immediate feedback eliminates the opportunity for students to practice monitoring for and learning from errors. Thus, they advocate delayed feedback. This article provides evidence that this line of reason is flawed and suggests that rather than focusing on the timing of feedback, instructional designers should focus on the “model of desired performance ” with respect to which feedback is provided. Instead of delaying feedback, we suggest broadening our model of correct behavior or desired performance to include some kinds of incorrect, but reasonable behaviors. This article explores the effects of providing feedback on the basis of a so-called intelligent novice cognitive model. A system based on an intelligent novice model allows students to make certain reasonable errors, and provides guidance through the exercise of error detection and correction skills. There are two pedagogical motivations for feedback based on an intelligent novice model. First, jobs today not only require ready-made expertise for dealing with known problems, but also intelligence to address novel situation where nominal experts are thrown back to the state of a novice. Second, the opportunity to reason about the causes and consequences of errors may allow students to form a better model of the behavior of domain operators. Results show that students receiving intelligent novice feedback acquire a deeper conceptual understanding of domain principles and demonstrate better transfer and retention of skills over time. Correspondence should be addressed to Santosh A. Mathan, 3660 Technology

Phishing attacks, in which criminals lure Internet users to websites that spoof legitimate websites, are occurring with increasing frequency and are causing considerable harm to victims. While a great deal of effort has been devoted to solving the phishing problem by prevention and detection of phishing emails and phishing websites, little research has been done in the area of training users to recognize those attacks. Our research focuses on educating users about phishing and helping them make better trust decisions. We identified a number of challenges for end-user security education in general and anti-phishing education in particular: users are not motivated to learn about security; for most users, security is a secondary task; it is difficult to teach people to identify security threats without also increasing their tendency to misjudge non-threats as threats. Keeping these challenges in mind, we developed an email-based anti-phishing education system called “PhishGuru ” and an online game called “Anti-Phishing Phil ” that teaches users how to use cues in URLs to avoid falling for phishing attacks. We applied learning science instructional principles in the design of PhishGuru and Anti-Phishing Phil. In this paper we present the results of PhishGuru and Anti-Phishing Phil user studies that demonstrate the effectiveness of these tools. Our results suggest that, while automated detection systems should be used as the first line of defense against phishing attacks, user education offers a complementary approach to help people better recognize fraudulent emails and websites.

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The recent introduction of computationally-enhanced tables that support simultaneous, multi-user input has important implications for co-located, face-to-face activity. Educational applications particularly stand to benefit from this new technology, which can combine the benefits of small group work with the enhancements offered by digital media. In this paper, we explore how the unique affordances of interactive tables provide a match for the needs of foreign language education, and how the design of tabletop software can be subtly altered to encourage desired educational outcomes. We present three prototype applications, and explore four design variations (feedback modality, feedback privacy, spatial configuration, and interaction visualizations) to assess their impact on student participation and self-assessment. We present observations of the use of our prototypes in two settings: (1) a controlled laboratory study and (2) authentic use by students as part of a language course at our university, and discuss our preliminary findings and avenues for future exploration. Author Keywords Educational interfaces, tabletop interfaces, computersupported cooperative learning, computer-supported cooperative work, co-located groupware. ACM Classification Keywords H5.3. Information interfaces and presentation (e.g., HCI):

Abstract. In this paper, we describe our on-going work on applying web mining to guide focused collaborative filtering for paper recommendations in a web-based learning system. In particular, we propose to first apply a data clustering technique on web usage data to form clusters (groups) of users with similar browsing patterns, which can be viewed as filtering based on implicit ratings (browsing sequences) according to [21]. Then, collaborative filtering techniques would be adopted on each cluster, instead of on the whole pool of users for recommendations as in other clustering-based collaborative filtering approaches. By using our two-layered collaborative filtering approach, we will not only maintain the diversity of users, but also focus on groups of users with similar browsing patterns. Therefore, our proposed approach could not only make personalized but also ‘grouplized ’ recommendations, thus overcoming previous claims that data clustering will only produce ‘less-personal recommendations ’ [33]. In addition, both explicit and implicit ratings are taken into consideration, which can reinforce and complement each other to make more accurate recommendations. 1.

recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Keywords: computational modeling, cognitive modeling, instructional theory, machine learning, learning science, second language learning, mathematics learning, science learning, robust learning, learning theory, knowledge componentsExecutive Summary The volume of research on learning and instruction is enormous. Yet progress in improving educational outcomes has been slow at best. Many learning science results have not been translated into general practice and it appears that most that have been fielded have not yielded significant results in randomized control trials. Addressing the chasm between learning science and educational practice will require massive efforts from many constituencies, but one of these efforts is to develop a theoretical framework that permits a more systematic accumulation of the relevant research base. A key piece in such a theoretical framework is the development of levels of analyses that are fine enough to be supported by cognitive science and cognitive neuroscience, but also at levels appropriate to guide the design of effective educational practices. An ideal scientific solution would be a small set of universal instructional principles that can be applied to produce efficient

ASSISTments is a web-based math tutor designed to address the need for timely student assessment while simultaneously providing instruction, thereby avoiding lost instruction time that typically occurs during assessment. This article presents a quasi-experiment that evaluates whether ASSISTments use has an effect on improving middle school students ’ year-end test scores. The data was collected from 1240 seventh graders in three treatment schools and one comparison school. Post-test (7th grade year-end test) results indicate, after adjusting for the pre-test (6th grade year-end test), that students in the treatment schools significantly outperformed students in the comparison school and the difference was especially present for special education students. A usage analysis reveals that greater student use of ASSISTments is associated with greater learning consistent with the hypothesis that it is useful as a tutoring system. We also found evidence consistent with the hypothesis that teachers adapt their whole class instruction based on overall