Automatic assessment of creativity in heuristic problem-solving based on query diversity

Indexación: Web of Science; Scopus.Research, development and innovation are the pillars on which companies rely to offer new products and services capable of attracting consumer demand. This is why creative problem-solving emerges as one of the most relevant skills of the 21st century. Fortunately, there are many creativity training programs that have proven effective. However, many of these programs and methods base on a previous measurement of creativity and require experienced reviewers, they consume time for being manual, and they are far from everyday activities. In this study, we propose a model to estimate the creative quality of users' solutions dealing with heuristic problems, based on the automatic analysis of query patterns issued during the information search to solve the problem. This model has been able to predict the creative quality of solutions produced by 226 users, reaching a sensitivity of 78.43%. Likewise, the level of agreement among reviewers in relation to the creative characteristics is evaluated through two rubrics, and thereby, observing the difficulties of the manual evaluation: subjectivity and effort. The proposed model could be used to foster prompt detection of non-creative solutions and it could be implemented in diverse industrial processes that can range from the recruitment of talent to the evaluation of performance in R&D&I processes.https://www.revistadyna.com/search/automatic-assessment-of-creativity-in-heuristic-problem-solving-based-on-query-diversit

Comparing instructional techniques on memory retention, retrieval, application and self-efficacy of grading criteria used for student self-assessment.

Purpose/Objectives: Study examined the effects of active engagement (ENG) and spaced retrieval practice (SRP) on the self-assessment (SA) competencies among D1 dental students. Methods: D1 dental students (N = 120) were randomly assigned to one of four treatment conditions (n=30) in this experimental 2X2 research study: no ENG/ no SRP (control); no ENG/ SRP; ENG/no SRP; ENG/ SRP. Outcomes included content mastery (30 item multiple-choice exam) and application knowledge (simulated dentoform assessment) of the course grading rubric for SA. Results: Results indicated a statistically significant main effect for ENG and SRP on both outcome variables. Specifically, ENG had a larger effect than SRP on both outcomes. A significant interaction effect was identified for ENG and SRP on content mastery. Students with ENG scored significantly higher (2.63 points) in content mastery with SRP than without SRP with a moderate effect size (d = 0.48). Students with no ENG scored significantly higher (5.57 points) in content mastery with SRP than without SRP with a large effect size (d = 1.39). Students with SRP scored significantly higher (3.93 points) in content mastery with ENG than without ENG with a moderate effect size (d = 0.63). Students with no SRP scored significantly higher (6.87 points) in content mastery with ENG than without ENG with a large effect size (d = 2.10). Conclusions: Findings shed light on the potential of active ENG and SRP on D1 dental students’ learning outcomes. Subsequent research is suggested on a wider range of dental students and learner topics

Faculty-Librarian Collaboration to Teach Research Skills: Electronic Symbiosis

This article discusses faculty-librarian collaboration to integrate technology in a course that focuses on teaching empirical research methodologies and library research skills to elementary and early childhood education graduate students. Vygotsky’s theory, standards in teacher education, and information literacy standards form the conceptual framework that supports this collaboration. The purpose and procedures of this collaboration, as well as student, faculty, and librarian outcomes, are discussed. This present collaboration on bibliographic instruction and the use of Blackboard courseware is framed within the context of past history of collaboration and future plans to expand this collaboration

Probing physics students' conceptual knowledge structures through term association

Traditional tests are not effective tools for diagnosing the content and structure of students' knowledge of physics. As a possible alternative, a set of term-association tasks (the "ConMap" tasks) was developed to probe the interconnections within students' store of conceptual knowledge. The tasks have students respond spontaneously to a term or problem or topic area with a sequence of associated terms; the response terms and timeof- entry data are captured. The tasks were tried on introductory physics students, and preliminary investigations show that the tasks are capable of eliciting information about the stucture of their knowledge. Specifically, data gathered through the tasks is similar to that produced by a hand-drawn concept map task, has measures that correlate with inclass exam performance, and is sensitive to learning produced by topic coverage in class. Although the results are preliminary and only suggestive, the tasks warrant further study as student-knowledge assessment instruments and sources of experimental data for cognitive modeling efforts.Comment: 31 pages plus 2 tables and 8 figure

A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts