Nonlinear physics opens a new paradigm for accurate transcription start site prediction.

There is evidence that DNA breathing (spontaneous opening of the DNA strands) plays a relevant role in the interactions of DNA with other molecules, and in particular in the transcription process. Therefore, having physical models that can predict these openings is of interest. However, this source of information has not been used before either in transcription start sites (TSSs) or promoter prediction. In this article, one such model is used as an additional information source that, when used by a machine learning (ML) model, improves the results of current methods for the prediction of TSSs. In addition, we provide evidence on the validity of the physical model, as it is able by itself to predict TSSs with high accuracy. This opens an exciting avenue of research at the intersection of statistical mechanics and ML, where ML models in bioinformatics can be improved using physical models of DNA as feature extractors.

Observation of Metacognitive Skills in Natural Environments: A Longitudinal Study With Mixed Methods.

Recent studies pointing to evaluation methods in natural environments suggest that their use in the analysis of metacognitive skills provides more precise information than the use of off-line evaluation methods. In this research, mixed methods are used over one academic year for the evaluation of the metacognitive skills that students of Secondary Education apply to solve physics problems. The objectives of this study are to analyze the use of metacognitive skills in natural environments and to study behavioral patterns of student learning through a longitudinal study. A total of 509 recordings of think-aloud protocols are analyzed through the categorization of the responses (liquefying) and the protocol of Van der Stel and Veenman for the analysis of the quality of metacognitive skills. Fewer conceptual errors and less uncertainty over vocabulary were noted during the academic year. Nevertheless, a degree of ambiguity persisted in the understanding of physics concepts. The metacognitive skills of Orientation and Planning were used more than any others. The technique of graph analysis is also applied, to establish the patterns of behavior of each student throughout the academic year. Different patterns were found, the analysis of which helped to identify academically challenged and at-risk students. The use of mixed observation techniques and graph analysis facilitated information on the pace of learning of each student. Future studies will be directed at proposals for the automation of these evaluation techniques in natural learning environments.

Differential efficacy of the resources used in b-learning environments / Eficacia diferencial de los recursos empleados en entornos b-learning

Background: Learning is increasingly frequent in B-Learning spaces. It is therefore necessary to study the characteristics that guarantee deeper and more successful learning in these learning environments. Method: We work with sample of 233 university students using the Moodle 3.1 platform in the third year of their degrees in Health Sciences. The effectiveness of four types of B-Learning on Learning Results (LR), behaviors on the platform, and student satisfaction are all studied. Prior knowledge is also used as a covariable. Results: It was found that the B-Learning environment in which the students obtained better general Outcomes Learning Results (LR) and a higher degree of satisfaction was the one that included the use of infographics and virtual laboratories based on Self-Regulated Learning (SRL). Conclusions: The design of B-Learning environments together with the use of SRL, is a factor that enhances effective learning and increases student satisfaction, especially if they include infographics and virtual laboratories. In addition, the use of these resources implements better overall LR on a larger number of students. Likewise, it promotes more homogeneous groups in the general LO. Future investigations will be aimed at verifying these results in other knowledge branches

Antecedentes: es cada vez más frecuente que el aprendizaje se realice en espacios B-Learning. Por ello, es preciso estudiar cuáles son las características que garantizan en estos entornos aprendizajes más profundos y exitosos. Método: se trabajó en la plataforma Moodle 3.1 con una muestra 233 estudiantes universitarios de tercero de grado en la rama de Ciencias de la Salud. Se estudió la efectividad de cuatro tipos de B-Learning sobre los Resultados de Aprendizaje (RA), las conductas de aprendizaje y la satisfacción de los estudiantes. Asimismo, se utilizó como covariable los conocimientos previos. Resultados: se halló que el entorno B-Learning en el que los estudiantes obtuvieron mejores RA generales y mayor grado de satisfacción fue el que incluía el uso de infografías y de laboratorios virtuales basados en aprendizaje autorregulado (SRL). Conclusiones: el diseño de entornos B-Learning, junto con la utilización de SRL, es un factor que potencia aprendizajes eficaces e incrementa la satisfacción de los estudiantes, especialmente si incluyen infografías y laboratorios virtuales. Además, el uso de estos recursos implementa mejores RA generales en un mayor número de estudiantes. Futuras investigaciones irán dirigidas a comprobar estos resultados en otras ramas de conocimiento

Does the Use of Learning Management Systems With Hypermedia Mean Improved Student Learning Outcomes?

Learning management systems (LMSs) that incorporate hypermedia Smart Tutoring Systems and personalized student feedback can increase self-regulated learning (SRL), motivation, and effective learning. These systems are studied with the following aims: (1) to verify whether the use of LMS with hypermedia Smart Tutoring Systems improves student learning outcomes; (2) to verify whether the learning outcomes will be grouped into performance clusters (Satisfactory, Good, and Excellent); and (3) to verify whether those clusters will group together the different learning outcomes assessed in four different evaluation procedures. Use of the LMS with hypermedia Smart Tutoring Systems was studied among students of Health Sciences, all of whom had similar test results in the use of metacognitive skills. It explained 38% of the variance in student learning outcomes in the evaluation procedures. Likewise, three clusters that grouped the learning outcomes in relation to the variable 'Use of an LMS with hypermedia Smart Tutoring Systems vs. No use' explained 60.4% of the variance. Each cluster grouped the learning outcomes in the different evaluation procedures. In conclusion, LMS with hypermedia Smart Tutoring Systems in Moodle increased the effectiveness of student learning outcomes, above all in the individual quiz-type tests. It also facilitated personalized learning and respect for the individual pace of student-learning. Hence, modules for the analysis of supervised, unsupervised and multivariate learning should be incorporated into the Moodle platform to provide teaching tools that will undoubtedly contribute to improvements in student learning outcomes. HIGHLIGHTS -Learning management systems (LMS) that incorporate hypermedia Smart Tutoring Systems and personalized student feedback can increase self-regulated learning (SRL).-Learning management systems with hypermedia Smart Tutoring Systems increased the effectiveness of student learning outcome.-The use of an LMS with hypermedia Smart Tutoring Systems vs. No use' explained 60.4% of the variance in student learning outcome.

How Do B-Learning and Learning Patterns Influence Learning Outcomes?

Learning Management System (LMS) platforms provide a wealth of information on the learning patterns of students. Learning Analytics (LA) techniques permit the analysis of the logs or records of the activities of both students and teachers on the on-line platform. The learning patterns differ depending on the type of Blended Learning (B-Learning). In this study, we analyse: (1) whether significant differences exist between the learning outcomes of students and their learning patterns on the platform, depending on the type of B-Learning [Replacement blend (RB) vs. Supplemental blend (SB)]; (2) whether a relation exists between the metacognitive and the motivational strategies (MS) of students, their learning outcomes and their learning patterns on the platform. The 87,065 log records of 129 students (69 in RB and 60 in SB) in the Moodle 3.1 platform were analyzed. The results revealed different learning patterns between students depending on the type of B-Learning (RB vs. SB). We have found that the degree of blend, RB vs. SB, seems to condition student behavior on the platform. Learning patterns in RB environments can predict student learning outcomes. Additionally, in RB environments there is a relationship between the learning patterns and the metacognitive and (MS) of the students.