This article describes research results based on multiple years of experimentation and real-world experience with an adaptive tutoring system named Wayang Outpost. The system represents a novel adaptive learning technology that has shown successful outcomes with thousands of students, and provided teachers with valuable information about students' mathematics performance. We define progress in three areas: improved student cognition, engagement, and affect, and we attribute this improvement to specific components and interventions that are inherently affective, cognitive, and metacognitive in nature. For instance, improved student cognitive outcomes have been measured with pre-post tests and state standardized tests, and achieved due to personalization of content and math fluency training. Improved student engagement was achieved by supporting students' metacognition and motivation via affective learning companions and progress reports, measured via records of student gaming of the system. Student affect within the tutor was measured through sensors and

Purpose -The purpose of this paper is to examine security incident response practices of information technology (IT) security practitioners as a diagnostic work process, including the preparation phase, detection, and analysis of anomalies. Design/methodology/approach -The data set consisted of 16 semi-structured interviews with IT security practitioners from seven organizational types (e.g. academic, government, and private). The interviews were analyzed using qualitative description with constant comparison and inductive analysis of the data to analyze diagnostic work during security incident response. Findings -The analysis shows that security incident response is a highly collaborative activity, which may involve practitioners developing their own tools to perform specific tasks. The results also show that diagnosis during incident response is complicated by practitioners' need to rely on tacit knowledge, as well as usability issues with security tools. Research limitations/implications -Owing to the nature of semi-structured interviews, not all participants discussed security incident response at the same level of detail. More data are required to generalize and refine the findings. Originality/value -The contribution of the work is twofold. First, using empirical data, the paper analyzes and describes the tasks, skills, strategies, and tools that security practitioners use to diagnose security incidents. The findings enhance the research community's understanding of the diagnostic work during security incident response. Second, the paper identifies opportunities for future research directions related to improving security tools.

A promising instructional approach corresponds to learning by observing others learn (i.e., by watching tutorial dialogue between a tutor and tutee). However, more work is needed to understand this approach's pedagogical utility. Thus, in 2 experiments we compared student learning from collaborative observation of dialogue with 2 other instructional contexts: 1-on-1 human tutoring and collaborative observation of monologue. In Study 1 (N ϭ 50), there was no significant difference in learning outcomes between the dialogue and tutoring conditions, while the dialogue condition was superior to the monologue condition. Study 2 (N ϭ 40), which involved a younger population than in Study 1, did not replicate these results, in that students learned less from observing dialogue than from being tutored, and there was no significant difference between the dialogue and monologue conditions. To shed light on our results, we analyzed the verbal data collected during the 2 experiments. This analysis showed that in Study 1, the dialogue observers generated more substantive contributions than did the monologue observers. In contrast, in Study 2 there was no significant difference between the observers in terms of substantive contributions; moreover, the total number of contributions was modest, which may have hindered observer learning in that study. In general, our findings suggest that collaboratively observing tutorial dialogue is a promising learning paradigm, but more work is needed to understand how to support young students to effectively learn in this paradigm.

Students who exploit properties of an instructional system to make progress while avoiding learning are said to be "gaming" the system. In order to investigate what causes gaming and how it impacts students, we analyzed log data from two Intelligent Tutoring Systems (ITS). The primary analyses focused on six college physics classes using the Andes ITS for homework and test preparation, starting with the research question: What is a better predictor of gaming, problem or student? To address this question, we developed a computational gaming detector for automatically labeling the Andes data, and applied several data mining techniques, including machine learning of Bayesian network parameters. Contrary to some prior findings, the analyses indicated that student was a better predictor of gaming than problem. This result was surprising, so we tested and confirmed it with log data from a second ITS (the Algebra Cognitive Tutor) and population (high school students). Given that student was more predictive of gaming than problem, subsequent analyses focused on how students gamed and in turn benefited (or not) from instructional features of the environment, as well as how gaming in general influenced problem solving and learning outcomes.

Abstract.We report the results of a randomized controlled evaluation of the effectiveness of pedagogical agents as providers of affective feedback. These digital learning companions were embedded in an intelligent tutoring system for mathematics, and were used by approximately one hundred students in two public high schools. Students in the control group did not receive the learning companions. Results indicate that low-achieving students-one third of whom have learning disabilities-had higher affective needs than their higherachieving peers; they initially considered math problem-solving more frustrating, less exciting, and felt more anxious when solving math problems. However, after they interacted with affective pedagogical agents, low-achieving students improved their affective outcomes, e.g., reported reduced frustration and anxiety.