A psychometric analysis of reliability and validity of the index of learning styles (ILS)

Prior literature showed that Felder and Silverman learning styles model (FSLSM) was widely adopted to cater to individual styles of learners whether in traditional or Technology Enhanced Learning (TEL). In order to infer this model, the Index of Learning Styles (ILS) instrument was proposed. This research aims to analyse the soundness of this instrument in an Arabic sample. Data were integrated from different courses and years. A total of 259 engineering students participated voluntarily in the study. The reliability was analysed by applying internal construct reliability, inter-scale correlation, and total item correlation. The construct validity was also considered by running factor analysis. The overall results indicated that the reliability and validity of perception and input dimensions were moderately supported, whereas processing and understanding dimensions showed low internal-construct consistency and their items were weakly loaded in the associated constructs. Generally, the instrument needs further effort to improve its soundness. However, considering the consistency of the produced results of engineering students irrespective of cross-cultural differences, it can be adopted to diagnose learning styles.

Science is Primary - Children Thinking and Learning in theChemistry Laboratory

The goal of primary science education is to foster children’s interest, develop positive science attitudes and promote science process skills development. Learning by playing and discovering provides several opportunities for children to inquiry and understand science based on the first–hand experience. The current research was conducted in the children’s laboratory in Heureka, the Finnish science centre. Young children (aged 7 years) which came from 4 international schools did a set of chemistry experiments in the laboratory. From the results of the cognitive test, the pre-test, the post-test, supported by observation and interview, we could make the conclusion that children enjoyed studying in the laboratory. Chemistry science was interesting and fascinating for young children; no major gender differences were found between boys and girls learning in the science laboratory. Lab work not only encouraged children to explore and investigate science, but also stimulated children’s cognitive development.

Machine translation and the disruption of foreign language learning activities

This study examines the question of how language teachers in a highly technology-friendly university environment view machine translation and the implications that this has for the personal learning environments of students. It brings an activity-theory perspective to the question, examining the ways that the introduction of new tools can disrupt the relationship between different elements in an activity system. This perspective opens up for an investigation of the ways that new tools have the potential to fundamentally alter traditional learning activities. In questionnaires and group discussions, respondents showed general agreement that although use of machine translation by students could be considered cheating, students are bound to use it anyway, and suggested that teachers focus on the kinds of skills students would need when using machine translation and design assignments and exams to practice and assess these skills. The results of the empirical study are used to reflect upon questions of what the roles of teachers and students are in a context where many of the skills that a person needs to be able to interact in a foreign language increasingly can be outsourced to laptops and smartphones.

Learning Grammar : A study of upper secondary level students’ attitudes and beliefs concerning the learning of grammar

The communicative approach to language learning is widely taught in Western education, and yet its predecessor, the grammar-translation method, is still commonly employed in other parts of the world. In Sweden, the increasing popularity of the communicative approach is often justified by the high level of students’ communicative skills (Öhman, 2013). At the same time, students’ written texts and speech contain many grammatical errors (Öhman, 2013). Consequently, being aware of their tendency to produce grammatical errors, some students express beliefs regarding both the explicit and implicit learning of grammar (Sawir, 2005; Boroujeni, 2012). The objective of this thesis is to gain more knowledge regarding students’ beliefs concerning the learning of English grammar at the upper secondary level, in Sweden. With this purpose a survey was conducted in two schools in Sweden, where 49 upper-secondary English students participated. Qualitative and quantitative methods were applied to process the collected data. Despite some difference in the participants’ ages, there were many similarities in their attitudes towards the teaching and learning of grammar. The results show that the participants in both schools believe that only by applying both, explicit and implicit methods, can they obtain a high level of language proficiency. The results of this study can help teachers in planning different activities that enhance the students’ knowledge of grammar.

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

The Impact of Teaching Presence in Intensive Online Courses on Perceived Learning and Sense of Community: A Mixed Methods Study

This mixed methods concurrent triangulation design study was predicated upon two models that advocated a connection between teaching presence and perceived learning: the Community of Inquiry Model of Online Learning developed by Garrison, Anderson, and Archer (2000); and the Online Interaction Learning Model by Benbunan-Fich, Hiltz, and Harasim (2005). The objective was to learn how teaching presence impacted students’ perceptions of learning and sense of community in intensive online distance education courses developed and taught by instructors at a regional comprehensive university. In the quantitative phase online surveys collected relevant data from participating students (N = 397) and selected instructional faculty (N = 32) during the second week of a three-week Winter Term. Student information included: demographics such as age, gender, employment status, and distance from campus; perceptions of teaching presence; sense of community; perceived learning; course length; and course type. The students claimed having positive relationships between teaching presence, perceived learning, and sense of community. The instructors showed similar positive relationships with no significant differences when the student and instructor data were compared. The qualitative phase consisted of interviews with 12 instructors who had completed the online survey and replied to all of the open-response questions. The two phases were integrated using a matrix generation, and the analysis allowed for conclusions regarding teaching presence, perceived learning, and sense of community. The findings were equivocal with regard to satisfaction with course length and the relative importance of the teaching presence components. A model was provided depicting relationships between and among teaching presence components, perceived learning, and sense of community in intensive online courses.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

International Service-Learning and the Development of Civic Identity: Understanding the Role of Short-Term International Service-Learning and its Influence on the Development of Civic Identity in College Students Using a Mixed Methodological Approach

Higher education has a responsibility to educate a democratic citizenry and recent research indicates civic engagement is on the decline in the United States. Through a mixed methodological approach, I demonstrate that the potential exists for well structured short-term international service-learning programming to develop college students’ civic identities. Quantitative analysis of questionnaire data, collected from American college students immediately prior to their participation in a short-term service-learning experience in Northern Ireland and again upon their return to the United States, revealed increases in civic accountability, political efficacy, justice oriented citizenship, and service-learning. Subsequent qualitative analysis of interview transcripts, student journals, and field notes suggested that facilitated critical reflection before, during, and after the experience promoted transformational learning. Emergent themes included: (a) responsibilities to others, (b) the value of international service-learning, (c) crosspollination of ideas, (d) stepping outside the daily routine to facilitate divergent thinking, and (e) the necessity of precursory thinking for sustaining transformations in thinking. The first theme, responsibilities to others, was further divided into subthemes of thinking beyond oneself, raising awareness of responsibility to others, and voting responsibly.

Engaged Learning: Enabling Self-Authorship and Effective Practice

There is now broad consensus that higher education must extend beyond content-based knowledge to encompass intellectual and practical skills, personal and social responsibility, and integrative learning. The college learning outcomes needed for success in 21st century life include critical thinking, a coherent sense of self, intercultural maturity, civic engagement, and the capacity for mutual relationships. Yet, research suggests that college students are struggling to achieve these outcomes in part because skills needed to succeed in college are not those needed to succeed upon graduation. One reason for this gap is that these college learning outcomes require complex developmental capacities or “self-authorship” that higher education is not currently designed to promote.

Defining Goals and Learning Objectives for a Longitudinal Clerkship in the Complex Context of Health Care

In autumn 2007 the Swiss Medical School of Berne (Switzerland) implemented mandatory short-term clerkships in primary health care for all undergraduate medical students. Students studying for a Bachelor degree complete 8 half-days per year in the office of a general practitioner, while students studying for a Masters complete a three-week clerkship. Every student completes his clerkships in the same GP office during his four years of study. The purpose of this paper is to show how the goals and learning objectives were developed and evaluated. Method:A working group of general practitioners and faculty had the task of defining goals and learning objectives for a specific training program within the complex context of primary health care. The group based its work on various national and international publications. An evaluation of the program, a list of minimum requirements for the clerkships, an oral exam in the first year and an OSCE assignment in the third year assessed achievement of the learning objectives. Results: The findings present the goals and principal learning objectives for these clerkships, the results of the evaluation and the achievement of minimum requirements. Most of the defined learning objectives were taught and duly learned by students. Some learning objectives proved to be incompatible in the context of ambulatory primary care and had to be adjusted accordingly. Discussion: The learning objectives were evaluated and adapted to address students’ and teachers’ needs and the requirements of the medical school. The achievement of minimum requirements (and hence of the learning objectives) for clerkships has been mandatory since 2008. Further evaluations will show whether additional learning objectives need to be adopte

Action Research and Learning Analytics in Higher Education

Teaching is a dynamic activity. It can be very effective, if its impact is constantly monitored and adjusted to the demands of changing social contexts and needs of learners. This implies that teachers need to be aware about teaching and learning processes. Moreover, they should constantly question their didactical methods and the learning resources, which they provide to their students. They should reflect if their actions are suitable, and they should regulate their teaching, e.g., by updating learning materials based on new knowledge about learners, or by motivating learners to engage in further learning activities. In the last years, a rising interest in ‘learning analytics’ is observable. This interest is motivated by the availability of massive amounts of educational data. Also, the continuously increasing processing power, and a strong motivation for discovering new information from these pools of educational data, is pushing further developments within the learning analytics research field. Learning analytics could be a method for reflective teaching practice that enables and guides teachers to investigate and evaluate their work in future learning scenarios. However, this potentially positive impact has not yet been sufficiently verified by learning analytics research. Another method that pursues these goals is ‘action research’. Learning analytics promises to initiate action research processes because it facilitates awareness, reflection and regulation of teaching activities analogous to action research. Therefore, this thesis joins both concepts, in order to improve the design of learning analytics tools. Central research question of this thesis are: What are the dimensions of learning analytics in relation to action research, which need to be considered when designing a learning analytics tool? How does a learning analytics dashboard impact the teachers of technology-enhanced university lectures regarding ‘awareness’, ‘reflection’ and ‘action’? Does it initiate action research? Which are central requirements for a learning analytics tool, which pursues such effects? This project followed design-based research principles, in order to answer these research questions. The main contributions are: a theoretical reference model that connects action research and learning analytics, the conceptualization and implementation of a learning analytics tool, a requirements catalogue for useful and usable learning analytics design based on evaluations, a tested procedure for impact analysis, and guidelines for the introduction of learning analytics into higher education.

New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering

This book provides the latest in a series of books growing out of the International Joint Conferences on Computer, Information and Systems Sciences and Engineering. It includes chapters in the most advanced areas of Computing, Informatics, Systems Sciences and Engineering. It has accessible to a wide range of readership, including professors, researchers, practitioners and students. This book includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Informatics, and Systems Sciences, and Engineering. It includes selected papers form the conference proceedings of the Ninth International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 2013). Coverage includes topics in: Industrial Electronics, Technology & Automation, Telecommunications and Networking, Systems, Computing Sciences and Software Engineering, Engineering Education, Instructional Technology, Assessment, and E-learning.

E-Learning Systems, Environments and Approaches

The volume consists of twenty-five chapters selected from among peer-reviewed papers presented at the CELDA (Cognition and Exploratory Learning in the Digital Age) 2013 Conference held in Fort Worth, Texas, USA, in October 2013 and also from world class scholars in e-learning systems, environments and approaches. The following sub-topics are included: Exploratory Learning Technologies (Part I), e-Learning social web design (Part II), Learner communities through e-Learning implementations (Part III), Collaborative and student-centered e-Learning design (Part IV). E-Learning has been, since its initial stages, a synonym for flexibility. While this dynamic nature has mainly been associated with time and space it is safe to argue that currently it embraces other aspects such as the learners’ profile, the scope of subjects that can be taught electronically and the technology it employs. New technologies also widen the range of activities and skills developed in e-Learning. Electronic learning environments have evolved past the exclusive delivery of knowledge. Technology has endowed e-Learning with the possibility of remotely fomenting problem solving skills, critical thinking and team work, by investing in information exchange, collaboration, personalisation and community building.

An innovative blended learning approach using virtual patients as preparation for skills laboratory training: perceptions of students and tutors

BACKGROUND Currently only a few reports exist on how to prepare medical students for skills laboratory training. We investigated how students and tutors perceive a blended learning approach using virtual patients (VPs) as preparation for skills training. METHODS Fifth-year medical students (N=617) were invited to voluntarily participate in a paediatric skills laboratory with four specially designed VPs as preparation. The cases focused on procedures in the laboratory using interactive questions, static and interactive images, and video clips. All students were asked to assess the VP design. After participating in the skills laboratory 310 of the 617 students were additionally asked to assess the blended learning approach through established questionnaires. Tutors' perceptions (N=9) were assessed by semi-structured interviews. RESULTS From the 617 students 1,459 VP design questionnaires were returned (59.1%). Of the 310 students 213 chose to participate in the skills laboratory; 179 blended learning questionnaires were returned (84.0%). Students provided high overall acceptance ratings of the VP design and blended learning approach. By using VPs as preparation, skills laboratory time was felt to be used more effectively. Tutors perceived students as being well prepared for the skills laboratory with efficient uses of time. CONCLUSION The overall acceptance of the blended learning approach was high among students and tutors. VPs proved to be a convenient cognitive preparation tool for skills training.

Dexamethasone aggravates hippocampal apoptosis and learning deficiency in pneumococcal meningitis in infant rats.

In an infant rat model of pneumococcal meningitis the effect of dexamethasone on neuronal injury in the hippocampus and on learning disability after recovery from the disease was examined. Treatment with dexamethasone or vehicle was started 18 h after infection, concomitant with antibiotics. Neuronal apoptosis in the hippocampal dentate gyrus 34 h after infection was significantly aggravated by dexamethasone treatment compared with vehicle controls (p = 0.02). Three weeks after acute pneumococcal meningitis, learning capacity of animals was assessed in the Morris water maze. The results showed a significantly impaired learning performance of infected animals treated with dexamethasone compared with vehicle controls (p = 0.01). Dexamethasone had no effect on hippocampal injury or learning in uninfected controls. Thus, dexamethasone as adjuvant therapy increased hippocampal cell injury and reduced learning capacity in this model of pneumococcal meningitis in infant rats.