Mining e-Learning domain concept map from academic articles

Recent researches have demonstrated the importance of concept map and its versatile applications especially in e-Learning. For example, while designing adaptive learning materials, designers need to refer to the concept map of a subject domain. Moreover, concept maps can show the whole picture and core knowledge about a subject domain. Research from literature also suggests that graphical representation of domain knowledge can reduce the problems of information overload and learning disorientation for learners. However, construction of concept maps typically relied upon domain experts in the past; it is a time consuming and high cost task. Concept maps creation for emerging new domains such as e-Learning is even more challenging due to its ongoing development nature. The aim of this paper is to construct e-Learning domain concept maps from academic articles. We adopt some relevant journal articles and conference papers in e-Learning domain as data sources, and apply text-mining techniques to automatically construct concept maps for e-Learning domain. The constructed concept maps can provide a useful reference for researchers, who are new to the e-Leaning field, to study related issues, for teachers to design adaptive learning materials, and for learners to understand the whole picture of e-Learning domain knowledge.     

The relationship between learning styles and cognitive traits – Getting additional information for improving student modelling

Student modelling is an important process for adaptive virtual learning environments. Student models include a range of information about the learners such as their domain competence, learning style or cognitive traits. To be able to adapt to the learners’ needs in an appropriate way, a reliable student model is necessary, but getting enough information about a learner is quite challenging. Therefore, mechanisms are needed to support the detection process of the required information. In this paper, we investigate the relationship between learning styles, in particular, those pertaining to the Felder–Silverman learning style model and working memory capacity, one of the cognitive traits included in the cognitive trait model. The identified relationship is derived from links between learning styles, cognitive styles, and working memory capacity which are based on studies from the literature. As a result, we demonstrate that learners with high working memory capacity tend to prefer a reflective, intuitive, and sequential learning style whereas learners with low working memory capacity tend to prefer an active, sensing, visual, and global learning style. This interaction can be used to improve the student model. Systems which are able to detect either only cognitive traits or only learning styles retrieve additional information through the identified relationship. Otherwise, for systems that already incorporate learning styles and cognitive traits, the interaction can be used to improve the detection process of both by including the additional information of a learning style into the detection process of cognitive traits and vice versa. This leads to a more reliable student model.     

In situ tuning of graphene oxide morphology by electrochemical exfoliation

Journal of Materials Science - This paper illustrates the electrochemical exfoliation of graphite for the synthesis of graphene oxide (GO) with few layers. Innovative design and arrangement of...     

The application of Simulation‐Assisted Learning Statistics (SALS) for correcting misconceptions and improving understanding of correlation

Simulation‐based computer assisted learning (CAL) is recommended to help students understand important statistical concepts, although the current systems are still far from ideal. Simulation‐Assisted Learning Statistics (SALS) is a simulation‐based CAL that is developed with a learning model that is based on cognitive conflict theory to correct misconceptions and enhance understanding of correlation. In this study, a mixed method (embedded experiment model) was utilized to examine the effects of SALS‐based learning compared with lecture‐based learning. The sample was composed of 72 grade‐12 students, who were randomly assigned to either the experimental group or the comparison group. The findings reveal that the SALS‐based learning approach is significantly more effective than lecture‐based learning, in terms of correcting students' misconceptions and improving their understanding of correlation. The study also uses quantitative and qualitative data to examine how the learning model of the SALS‐based learning approach contributes to the enhanced learning outcomes. Finally, practical suggestions were made with regard to directions for future studies.     

Superior electrical,mechanical and viscoelastic properties of CNTs coated carbon textile reinforced phenolic composite for high-performance structural applications

In the present study, carbon nanotubes (CNTs) were grown up on nickel-coated carbon textiles utilizing acetylene gas as a carbon precursor in an isothermal CVD reactor. The effects of CNT coating time (0 to 25 min) in the carbon textile-phenolic composites (CPCs) prepared via conventional hand-layup technique followed by compression molding were assessed using microstructures, static and dynamic mechanical thermal analysis, density and electrical properties. Significant improvement was observed in static as well as dynamic mechanical properties and electrical properties for all the CNT coated samples. Though, 15 min coating time gives the optimum results. The results showed ~71, 74, 62, 67, 38, and 45% increment in storage modulus, loss modulus, flexural strength, flexural modulus, tensile strength, Young's modulus, respectively. Furthermore, huge improvement (707%) in electrical conductivity and significant enrichment in thermal stability mark CNTs coated carbon textile as the efficient alternative reinforcement for high-performance thermo-structural applications such as aerospace and automotive fields.     

Augmenting paper-based reading activity with direct access to digital materials and scaffolded questioning

Comprehension is the goal of reading. However, students often encounter reading difficulties due to the lack of background knowledge and proper reading strategy. Unfortunately, print text provides very limited assistance to one’s reading comprehension through its static knowledge representations such as symbols, charts, and graphs. Integrating digital materials and reading strategy into paper-based reading activities may bring opportunities for learners to make meaning of the print material. In this study, QR codes were adopted in association with mobile technology to deliver supplementary materials and questions to support students’ reading. QR codes were printed on paper prints to provide direct access to digital materials and scaffolded questions. Smartphones were used to scan the printed QR codes to fetch pre-designed digital resources and scaffolded questions over the Internet. A quasi-experiment was conducted to evaluate the effectiveness of direct access to the digital materials prepared by the instructor using QR codes and that of scaffolded questioning in improving students’ reading comprehension. The results suggested that direct access to digital resources using QR codes does not significantly influence students’ reading comprehension; however, the reading strategy of scaffolded questioning significantly improves students’ understanding about the text. The survey showed that most students agreed that the integrated print-and-digital-material- based learning system benefits English reading comprehension but may not be as efficient as expected. The implications of the findings shed light on future improvement of the system.     

Machine intelligence-driven classification of cancer patients-derived extracellular vesicles using fluorescence correlation spectroscopy: results from a pilot study

Neural Computing and Applications - Patient-derived extracellular vesicles (EVs) that contains a complex biological cargo is a valuable source of liquid-biopsy diagnostics to aid in early...     

Analysis of learners' navigational behaviour and their learning styles in an online course

Providing adaptive features and personalized support by considering students' learning styles in computer‐assisted learning systems has high potential in making learning easier for students in terms of reducing their efforts or increasing their performance. In this study, the navigational behaviour of students in an online course within a learning management system was investigated, looking at how students with different learning styles prefer to use and learn in such a course. As a result, several differences in the students' navigation patterns were identified. These findings have several implications for improving adaptivity. First, they showed that students with different learning styles use different strategies to learn and navigate through the course, which can be seen as another argument for providing adaptivity. Second, the findings provided information for extending the adaptive functionality in typical learning management systems. Third, the information about differences in navigational behaviour can contribute towards automatic detection of learning styles, helping in making student modeling approaches more accurate.