A Smart Collaborative Educational Game with Learning Analytics to Support English Vocabulary Teaching

Learning Analytics (LA) approaches have proved to be able to enhance learning process and learning performance. However, little is known about applying these approaches for second language acquisition using educational games. Therefore, this study applied LA approaches to design a smart collaborative educational game, to enhance primary school children learning English vocabularies. Specifically, the game provided dashboards to the teachers about their students in a real-time manner. A pilot experiment was conducted in a public primary school where the students’ data from experimental and control groups, namely learning and motivation test scores, interview and observation, were collected and analyzed. The obtained results showed that the experimental group (who used the smart game with LA) had significantly higher motivation and performance for learning English vocabularies than the control group (who used the smart game without LA). The findings of this study can help researchers and practitioners incorporate LA in their educational games to help students enhance language acquisition

Racial and Geographic Variation in Effects of Maternal Education and Neighborhood-Level Measures of Socioeconomic Status on Gestational Age at Birth: Findings From the ECHO Cohorts

Preterm birth occurs at excessively high and disparate rates in the United States. In 2016, the National Institutes of Health (NIH) launched the Environmental influences on Child Health Outcomes (ECHO) program to investigate the influence of early life exposures on child health. Extant data from the ECHO cohorts provides the opportunity to examine racial and geographic variation in effects of individual- and neighborhood-level markers of socioeconomic status (SES) on gestational age at birth. The objective of this study was to examine the association between individual-level (maternal education) and neighborhood-level markers of SES and gestational age at birth, stratifying by maternal race/ethnicity, and whether any such associations are modified by US geographic region. Twenty-six ECHO cohorts representing 25,526 mother-infant pairs contributed to this disseminated meta-analysis that investigated the effect of maternal prenatal level of education (high school diploma, GED, or less; some college, associate\u27s degree, vocational or technical training [reference category]; bachelor\u27s degree, graduate school, or professional degree) and neighborhood-level markers of SES (census tract [CT] urbanicity, percentage of black population in CT, percentage of population below the federal poverty level in CT) on gestational age at birth (categorized as preterm, early term, full term [the reference category], late, and post term) according to maternal race/ethnicity and US region. Multinomial logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CIs). Cohort-specific results were meta-analyzed using a random effects model. For women overall, a bachelor\u27s degree or above, compared with some college, was associated with a significantly decreased odds of preterm birth (aOR 0.72; 95% CI: 0.61-0.86), whereas a high school education or less was associated with an increased odds of early term birth (aOR 1.10, 95% CI: 1.00-1.21). When stratifying by maternal race/ethnicity, there were no significant associations between maternal education and gestational age at birth among women of racial/ethnic groups other than non-Hispanic white. Among non-Hispanic white women, a bachelor\u27s degree or above was likewise associated with a significantly decreased odds of preterm birth (aOR 0.74 (95% CI: 0.58, 0.94) as well as a decreased odds of early term birth (aOR 0.84 (95% CI: 0.74, 0.95). The association between maternal education and gestational age at birth varied according to US region, with higher levels of maternal education associated with a significantly decreased odds of preterm birth in the Midwest and South but not in the Northeast and West. Non-Hispanic white women residing in rural compared to urban CTs had an increased odds of preterm birth; the ability to detect associations between neighborhood-level measures of SES and gestational age for other race/ethnic groups was limited due to small sample sizes within select strata. Interventions that promote higher educational attainment among women of reproductive age could contribute to a reduction in preterm birth, particularly in the US South and Midwest. Further individual-level analyses engaging a diverse set of cohorts are needed to disentangle the complex interrelationships among maternal education, neighborhood-level factors, exposures across the life course, and gestational age at birth outcomes by maternal race/ethnicity and US geography

On the use of case-based planning for e-learning personalization

This is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 60, 1-15, 2016. DOI:10.1016/j.eswa.2016.04.030In this paper we propose myPTutor, a general and effective approach which uses AI planning techniques to create fully tailored learning routes, as sequences of Learning Objects (LOs) that fit the pedagogical and students’ requirements. myPTutor has a potential applicability to support e-learning personalization by producing, and automatically solving, a planning model from (and to) e-learning standards in a vast number of real scenarios, from small to medium/large e-learning communities. Our experiments demonstrate that we can solve scenarios with large courses and a high number of students. Therefore, it is perfectly valid for schools, high schools and universities, especially if they already use Moodle, on top of which we have implemented myPTutor. It is also of practical significance for repairing unexpected discrepancies (while the students are executing their learning routes) by using a Case-Based Planning adaptation process that reduces the differences between the original and the new route, thus enhancing the learning process. © 2016 Elsevier Ltd. All rights reserved.This work has been partially funded by the Consolider AT project CSD2007-0022 INGENIO 2010 of the Spanish Ministry of Science and Innovation, the MICINN project TIN2011-27652-C03-01, the MINECO and FEDER project TIN2014-55637-C2-2-R, the Mexican National Council of Science and Technology, the Valencian Prometeo project II/2013/019 and the BW5053 research project of the Free University of Bozen-Bolzano.Garrido Tejero, A.; Morales, L.; Serina, I. (2016). On the use of case-based planning for e-learning personalization. Expert Systems with Applications. 60:1-15. https://doi.org/10.1016/j.eswa.2016.04.030S1156

Educational system based on simulation and intelligent conversation

This paper explores the use of intelligent conversation and simulation to increase learning outcomes of science topics, i.e. Physics in our case. The underlying theory is that educational 3D interactive simulations can enhance learning by, first of all, adding a playful element to the learning process which boosts motivation, and, second, by creating an opportunity to \u27learn by doing\u27 when enabling the learner to explore the simulated phenomena by themselves. Furthermore, conversational agents can enhance and strengthen the simulation-based learning environment by playing the role of guide or tutor, e.g. offering hints when the student is struggling, as well as evaluator, which is needed to provide appropriate feedback and guidance. Subsequently, a prototype of an educational system that combines a Physics simulation and a conversational agent was developed and then evaluated. In order to evaluate the effectiveness of the proposed educational system to induce learning gains, we conducted a randomized controlled trial experiment. In particular, we compared the following experimental conditions: using a simulation alone followed by a full system that included both the simulation and a conversational agent versus the conversational agent alone followed by the full system. In both conditions, students were exposed to the same content

Heavy Metal Exposures, C-Reactive Protein, and Dietary Inflammatory Index: NHANES 2007-2010

PURPOSE: This study utilized data from National Health and Nutrition Examination Survey 2007-2010 to investigate the relationship between heavy metal exposures (lead (Pb), cadmium (Cd), and mercury (Hg)), dietary inflammatory index (DII), and C-reactive protein (CRP) as a biomarker for inflammation. SUBJECTS: The study comprised a nationally representative sample of n = 7,407 adults age 20+, excluding pregnant women and participants with CRP values ≥10 mg/dL. METHODS AND MATERIALS: Data was utilized from continuous NHANES cycles 2007 to 2008 and 2009 to 2010, harmonized according to procedures outlined by the NCHS. ANALYSES: Survey-weighted, covariate-adjusted linear regression models were produced to assess the relationship between heavy metal exposures, DII, and natural log-transformed CRP. RESULTS: We found significant positive association between heavy metals and CRP only in specific subgroups of the population after additionally adjusting for age, annual household income, and history of inflammatory disease. Pb was only significantly positively associated with CRP in individuals of normal weight, and Cd was only significantly positively associated with CRP in non-Hispanic white males (p \u3c 0.05). Hg was not significantly positively associated with CRP in any subgroup; however, the effect of Hg on CRP was found to be dependent on DII, with higher DII levels associated with increasing positivity of the association between Hg and CRP (never achieving statistical significance). CONCLUSIONS: The results of this study highlight the complexity of the relationships between heavy metal exposures, diet, and inflammatory processes, and underscore the need for further research to elucidate the mechanisms behind these relationships