We present an approach for acquiring common sense knowledge from social interaction. We argue that social common sense should be learned from daily interactions using implicit user's feedbacks and requires shared understanding of social situations. A service-oriented architecture, inspired from cognitive science, that foster mutual understanding between a smart environment and its inhabitants is presented. The method makes use of ConceptNet to work with common sense knowledge. We are able to successfully use and learn common sense knowledge.

In order to understand the role of metacognition and self-regulation in student learning, 35 college students were asked to solve problems in college linear algebra and in remedial math using Cognitive Constructor. Results reveal the predominance of forward chaining in problem solving.

A seminal study conducted by Greene, Bolick, and Robertson (2010) showed that learners do not always engage in appropriate metacognitive and self-regulatory processes while learning about history. However, little research exists to guide the design of technology-rich learning environments (TRLEs) as metacognitive tools in social sciences education. In order to address this issue, we designed a metacognitive tool using a bottom-up approach (Poitras, 2010; Poitras, Lajoie, & Hong, in prep). Thirty-two undergraduate students read an historical narrative text either with or without the benefit of the metacognitive tool. Results from process and product data suggest that learners had better recall because the metacognitive tool assisted learners to (a) notice that particular events are unexplained in the circumstances described in an historical narrative text, and (b) generate hypothetical causes to explain the occurrence of such events. We discuss the implications of these findings for the development of the MetaHistoReasoning Tool, a TRLE that assists learners’ historical reasoning while they accomplish authentic tasks of historical inquiry.

A well-documented finding in the help-seeking literature is that especially those learners who need it the most do not seek help (appropriately). In this exploratory study, we investigated re-reading as a unique window into elementary help-seeking processes. Students had to learn the content of multiple hypertext pages of different complexity for a subsequent knowledge test. After this learning phase we randomly assigned learners to two experimental groups: The memory control group (MG, n = 14) directly answered the knowledge test and the experimental help-seeking group (HSG, n = 15) had the option to re-read the hypertext pages before answering. Results show that HSG students outperformed MG students and that HSG students strongly adapted the extent and frequency of their re-reading to task complexity and the complexity of the hypertext pages. However, more re-reading or more adaptivity did not automatically enhance performance on the knowledge test. The implications of these findings for the design of adaptive (metacognitive) help functions in computer-based learning environments will be discussed.

Meta-cognitive skills to be developed in learning for the 21st century is the detection and correction of errors in solutions. These meta-cognitive skills can help to detect errors the learner has made her/himself as well as errors others have made. Our investigations in learning from errors have the ultimate goal to adapt the selection and presentation to the learner so that he/she can better learn from erroneous examples others have made. In our experiments we found that (1) erroneous examples with help provision can promote students skill of find errors, (2) the benefit from erroneous examples depends on the relation between the student's level and the example's difficulty, i.e. if the student is prepared for the problem, (3) for many students it is very difficult to correct errors.

Learners that have better metacognition acquire knowledge faster than others who do not. If we had better models of such learning, we would be able to build a better metacognitive educational system. In this paper, we propose a computational model that uses a probabilistic context free grammar induction algorithm yielding metacognitive learning by acquiring deep features to assist future learning. We discuss the challenges of integrating this model into a synthetic student, and possible future studies in using this model to better understand human learning. Preliminary results suggest that both stronger prior knowledge and a better learning strategy can speed up the learning process. Some model variations generate human-like error pattern.

Developing the capabilities of children to comprehend written texts is key to their development as young adults. Text comprehension skills develop enormously from the age of 7- 8 until the age of 11. Nowadays, several young children (˜5% – 10% of novice readers) turn out to be poor (text) comprehenders: they demonstrate text comprehension difficulties, related to inference-making skills, despite proficiency in lowlevel cognitive skills like word decoding. Though there are several pencil-and-paper reading interventions for improving inference-making skills on text, and addressed to poor comprehenders, the design and evaluation of Adaptive Learning Systems (ALSs) are lagging behind. The use of more intelligent ALSs to custom-tailor such interventions in the form of games for poor comprehenders has tremendous potential. Our system embodies that potential. This paper presents the design of our ALS by focusing on its intelligent adaptive engine and the related conceptual models, and by presenting the visual interfaces for story telling and gaming.

A new architectural framework for a metacognitive tutoring system is presented that is aimed to stimulate self-regulatory behavior in the learner.The new framework extends the cognitive architecture of TutorJ that has been already proposed by some of the authors. TutorJ relies mainly on dialogic interaction with the user, and makes use of a statistical dialogue planner implemented through a Partially Observable Markov Decision Process (POMDP). A suitable two-level structure has been designed for the statistical reasoner to cope with measuring and stimulating metacognitive skills in the user. Suitable actions have been designed to this purpose starting from the analysis of the main questionnaires proposed in the literature. Our reasoner has been designed to model the relation between each item in a questionnaire and the related metacognitive skill, so the proper action can be selected by the tutoring agent. The complete framework is detailed, the reasoner structure is discussed, and a simple application scenario is presented.

An experiment was conducted to test the efficacy of a new intelligent hypermedia system, MetaTutor, which is intended to prompt and scaffold the use of self-regulated learning (SRL) processes during learning about a human body system. Sixty-eight (N=68) undergraduate students learned about the human circulatory system under one of three conditions: prompt and feedback (PF), prompt-only (PO), and control (C) condition. The PF condition received timely prompts from animated pedagogical agents to engage in planning processes, monitoring processes, and learning strategies and also received immediate directive feedback from the agents concerning the deployment of the processes. The PO condition received the same timely prompts, but did not receive any feedback following the deployment of the processes. Finally, the control condition learned without any assistance from the agents during the learning session. All participants had two hours to learn using a 41-page hypermedia environment which included texts describing and static diagrams depicting various topics concerning the human circulatory system. Results indicate that the PF condition had significantly higher learning efficiency scores, when compared to the control condition. There were no significant differences between the PF and PO conditions. These results are discussed in the context of development of a fully-adaptive hypermedia learning system intended to scaffold self-regulated learning.

Successful learning with advanced learning technologies is based on the premise that learners adaptively regulate their cognitive and metacognitive behaviors during learning. However, there is abundant empirical evidence that suggests that learners typically do not adaptively modify their behavior, thus suggesting that they engage in what is called dysregulated behavior. Dysregulated learning is a new term that is used to describe a class of behaviors that learners use that lead to minimal learning. Examples of dysregulated learning include failures to: (1) encode contextual demands, (2) deploy effective learning strategies, (3) modify and update internal standards, (4) deal with the dynamic nature of the task, (5) metacognitive monitor the use of strategies and repeatedly make accurate metacognitive judgments, and (6) intelligently adapt behavior during learning so as to maximize learning and understanding of the instructional material. Understanding behaviors associated with dysregulated learning is critical since it has implications for determining what they are, when they occur, how often they occur, and how they can be corrected during learning.