Inferring Concept Prerequisite Relations from Online Educational Resources

The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ, a new supervised learning method for inferring concept prerequisite relations. PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture. PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data. It outperforms state-of-the-art approaches on benchmark datasets and can effectively learn from very less training data. PREREQ can also use unlabeled video playlists, a steadily growing source of training data, to learn concept prerequisites, thus obviating the need for manual annotation of course prerequisites.Comment: Accepted at the AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI-19

Critical-Thinking Skills of First-Year Athletic Training Students Enrolled in Professional Programs

Context: The Examination of Professional Degree Level document presented to the National Athletic Trainers’ Association Board of Directors states that research in athletic training education has not investigated differences in the critical-thinking skills of professional athletic training students. Objective: Investigate the differences in critical thinking and other demographic variables across first-year athletic training students enrolled in professional bachelor’s- and master’s-degree programs. Design: Quantitative study. Setting: District 10 athletic training programs. Patients or Other Participants: Students (N ¼ 40) enrolled within their first 6 months of a professional athletic training program were asked to complete the California Critical Thinking Skills Test (CCTST). Twelve first-year master’s-degree students (8 female, 4 male) and 28 bachelor’s-degree students (18 female, 10 male) completed the CCTST (age ¼ 20.73 6 3.09 years). Main Outcome Measure(s): Athletic training students in District 10 were asked to complete the CCTST during the first 6 months of their respective programs. Independent t tests were used to evaluate the difference in critical-thinking scores between professional master’s- and bachelor’s-degree athletic training students. A 1-way analysis of variance was conducted to determine differences in critical-thinking skills with regard to gender, age, and parental educational level. Results: There were no statistically significant differences in critical-thinking skills between bachelor’s- and master’s-degree athletic training students enrolled in a professional athletic training program (P ¼ .991). Additionally, there were no statistically significant differences in critical-thinking skills with regard to gender (P ¼ .156), age (P ¼ .410), or parental education level (P ¼ .156). Conclusions: The results suggest master’s students do not have greater critical-thinking skills than professional bachelor’s students before engaging in athletic training education. Therefore, as the professional degree of athletic training transitions to the graduate level, athletic training educators may need to investigate and use pedagogical practices that will graduate critically thinking athletic trainers

Data mining technology for the evaluation of learning content interaction

Interactivity is central for the success of learning. In e-learning and other educational multimedia environments, the evaluation of interaction and behaviour is particularly crucial. Data mining – a non-intrusive, objective analysis technology – shall be proposed as the central evaluation technology for the analysis of the usage of computer-based educational environments and in particular of the interaction with educational content. Basic mining techniques are reviewed and their application in a Web-based third-level course environment is illustrated. Analytic models capturing interaction aspects from the application domain (learning) and the software infrastructure (interactive multimedia) are required for the meaningful interpretation of mining results

Pre-Education Programs: A Comprehensive Project at Henry Ford Community College

Henry Ford Community College (HFCC) in Dearbom, Michigan is a two-year institution of higher education serving a diverse student population of approximately 13,000 students. In addition to providing a broad array of technical and vocational programs, the College provides the first two years of a baccalaureate program. However, the transferability of these programs is not assured. In the absence of a mandated state-wide curriculum, two- and four-year colleges and universities in Michigan develop courses and programs independently, and the transfer of courses between institutions is determined independently by the respective departments. The end result is often loss of credit when a community college student transfers. Other problems faced prospective education majors as well. Students were justifiably apprehensive about the suitability of their academic preparation for the challenges they would confront at the four-year institution. To address this and other problems, HFCC initiated a comprehensive project to develop a structured teacher education program. The project consisted of two components: 1) creation of pre-education programs and 2) institution of articulated transfer agreements as a result of collaboration with neighboring universities. The success of this reform is underscored by a dramatic increase in HFCC pre-education majors from 354 students in 1994 to 697 in 1997, with 80 students designating minority status in 1994 and 179 in 1997

On Cognitive Preferences and the Plausibility of Rule-based Models

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus on plausibility and relation to interpretability, comprehensibility, and justifiabilit

Different Methods of Embodied Cognition in Pedagogy and its Effectiveness in Student Learning

The Mathematical Ideas Analysis hypothesizes that abstract mathematical reasoning is unconsciously organized and integrated with sensory-motor experience. Basic research testing movement, language, and perception during math problem solving supports this hypothesis. Applied research primarily measures students’ performance on math tests after they engage in analogous sensory-motor tasks, but findings show mixed results. Sensory-motor tasks are dependent on several moderators (e.g., instructional guidance, developmental stage) known to help students learn, and studies vary in how each moderator is implemented. There is little research on the effectiveness of sensory-motor tasks without these moderators. This study compares different approaches to working with an interactive application designed to emulate how people intrinsically solve algebraic equations. A total of 130 participants (84 females, 54 males) were drawn from a pool of Introductory Psychology students attending San Jose State University. Participants were placed in three different learning environments, and their performance was measured by comparing improvement between a pre-test and a post-test. We found no difference between participants who worked alone with the application, were instructed by the experimenter while using the application, or who instructed the experimenter on how to solve equations using the application. Further research is needed to examine how and whether analogous sensory-motor interfaces are a useful learning tool, and if so, what circumstances are ideal for sensory-motor interfaces to be used

The ITALK project : A developmental robotics approach to the study of individual, social, and linguistic learning

This is the peer reviewed version of the following article: Frank Broz et al, “The ITALK Project: A Developmental Robotics Approach to the Study of Individual, Social, and Linguistic Learning”, Topics in Cognitive Science, Vol 6(3): 534-544, June 2014, which has been published in final form at doi: http://dx.doi.org/10.1111/tops.12099 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." Copyright © 2014 Cognitive Science Society, Inc.This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.Peer reviewe