International Journal of Artificial Intelligence in Education - Volume 20, issue 4

Authors: Hausmann, Robert G. M. | VanLehn, Kurt

Article Type: Research Article

Abstract: Self-explaining is a domain-independent learning strategy that generally leads to a robust understanding of the domain material. However, there are two potential explanations for its effectiveness. First, self-explanation generates additional content that does not exist in the instructional materials. Second, when compared to comprehension, generation of content increases understanding and recall. An in vivo experiment was designed to distinguish between these potentially orthogonal hypotheses. Students were instructed to use one of two learning strategies, self-explaining and paraphrasing, to study either a completely justified example or an incomplete example. Learning was assessed at multiple time points and levels of granularity. The …results were consistent, favoring the generation account of self-explanation. This suggests that examples should be designed to encourage the active generation of missing content information. Show more

Keywords: Self-explanation, physics, worked-out examples, problem solving

DOI: 10.3233/JAI-2010-010

Citation: International Journal of Artificial Intelligence in Education, vol. 20, no. 4, pp. 303-332, 2010

Authors: Baschera, Gian-Marco | Gross, Markus

Article Type: Research Article

Abstract: We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification (local) and prediction of further performance (global). The inference algorithm has been employed in a student model for spelling with a detailed set of letter and phoneme based mal-rules. The local and global information about the student allows for appropriate remediation actions to adapt to their needs. The error classification, student model prediction …and the efficacy of the adapted remediation actions have been validated on the data of two large-scale user studies. The enhancement of the spelling training based on the novel student model resulted a significant increase in the student learning performance. Show more

Keywords: Inference, poisson regression, spelling, error classification

DOI: 10.3233/JAI-2010-011

Citation: International Journal of Artificial Intelligence in Education, vol. 20, no. 4, pp. 333-360, 2010

Authors: D'Mello, Sidney K. | Lehman, Blair | Person, Natalie

Article Type: Research Article

Abstract: We explored the affective states that students experienced during effortful problem solving activities. We conducted a study where 41 students solved difficult analytical reasoning problems from the Law School Admission Test. Students viewed videos of their faces and screen captures and judged their emotions from a set of 14 states (basic emotions, learning-centered emotions, and neutral) at relevant points in the problem solving process (after new problem is displayed, in the midst of problem solving, after feedback is received). The results indicated that curiosity, frustration, boredom, confusion, happiness, and anxiety were the major emotions that students experienced, while contempt, anger, …sadness, fear, disgust, eureka, and surprise were rare. Follow-up analyses on the temporal dynamics of the emotions, their contextual underpinnings, and relationships to problem solving outcomes supported a general characterization of the affective dimension of problem solving. Affective states differ in: (a) their probability of occurrence as regular, routine, or sporadic emotions, (b) their temporal dynamics as persistent or random emotions, (c) their characterizations as product or process related emotions, and (d) whether they were positively or negatively related to problem solving outcomes. A synthesis of our major findings, limitations, resolutions, and implications for affect-sensitive artificial learning environments are discussed. Show more

Keywords: Affective states, emotions, problem solving, LSAT, intelligent tutoring systems

DOI: 10.3233/JAI-2010-012

Citation: International Journal of Artificial Intelligence in Education, vol. 20, no. 4, pp. 361-389, 2010