This work is devoted to the analysis of the performance of energy detection based spectrum sensing in the presence of enriched fading conditions which are distinct for the large number of multipath components and the lack of a dominant components. This type of fading conditions are characterized efficiently by the well known Nakagami-q or Hoyt distribution and the proposed analysis is carried out in the context of the area under the receiver operating characteristics (ROC) curve (AUC). Unlike the widely used probability of detection metric, the AUC is a single metric and has been shown to be rather capable of evaluating the performance of a detector in applications relating to cognitive radio, radar systems and biomédical engineering, among others. Based on this, novel analytic expressions are derived for the average AUC and its complementary metric, average CAUC, for both integer and fractional values of the involved time-bandwidth product. The derived expressions have a tractable algebraic representation which renders them convenient to handle both analytically and numerically. Based on this, they are employed in analyzing the behavior of energy detection based spectrum sensing over enriched fading conditions for different severity scenarios, which demonstrates that the performance of energy detectors is, as expected, closely related to the value of the fading parameter q.
Augmented reality tools and applications have been shown to have powerfully impelled the development of the field of education. In this article, the authors designed and developed an augmented reality technology-based courseware “Starry Sky Exploration—Eight Planets in the Solar System” and explored how AR can bring an immersive learning experience to students and improve students' learning effectiveness. This article presents and evaluates AR courseware applicable for the geography curriculum in secondary schools in China. In this study, 36 students from Shanghai secondary vocational school were invited to participate in the experiment, the authors use reliability analysis, regression analysis and brainwave analysis to evaluate the effectiveness of the AR course. The authors found that students have higher learning satisfaction and behavioral willingness in AR-based experiential learning activities. It can be seen that AR helps to stimulate students' interest in learning.
Programming education has recently received increased attention due to growing demand for programming and information technology skills. However, a lack of teaching materials and human resources presents a major challenge to meeting this demand. One way to compensate for a shortage of trained teachers is to use machine learning techniques to assist learners. This article proposes a learning path recommendation system that applies a recurrent neural network to a learner's ability chart, which displays the learner's scores. In brief, a learning path is constructed from a learner's submission history using a trial-and-error process, and the learner's ability chart is used as an indicator of their current knowledge. An approach for constructing a learning path recommendation system using ability charts and its implementation based on a sequential prediction model and a recurrent neural network, are presented. Experimental evaluation is conducted with data from an e-learning system.
In this article, a cognitive framework for observing learning activities based on human-computer coupling is proposed. The observation is based on the vectorization of a learning situation along with human-computer interaction factors. An evolutionary high-dimensional topology of learning cognitive flow is introduced for human-computer interaction. In addition, the authors have selected a tree topology as the topological structure of a low-dimensional learning space to process the observations for online learning. Furthermore, the mechanism for the BSM (brain cognitive body-situation of coupling-manifold of information) the coupling morphism is presented. The principle for the coupled observation of objects in a cognitive or learning manifold is proposed. Finally, a special system for teaching and learning is programmed to observe and evaluate learning and mental arithmetic training processes. This system not only provides students with a new ergonomic learning model but also records the students' learning processes. Thus, the teachers can summarize the knowledge points automatically rather than manually.
This study aims to create learning path navigation for target learners by discovering the correlation among micro-learning units. In this study, the learning path is defined as a sequence of learning units used to realize a learning goal, and a period used for realizing the learning goal is regarded as a learning cycle. Furthermore, the learning unit datasets are extracted according to the learning cycle. In order to discover the correlations of learning units, we proposed an algorithm named Bayesian Network Association Rule (BNAR), which is used to establish a dynamic learning path according to the learning history of reference learners group who achieved learning goals. Based on the successful learning history, the dynamic learning path navigation will help target learners to improve learning efficiency.
Adaptive curriculum sequencing (ACS) is still a challenge in the adaptive learning field. ACS is a NP-hard problem especially considering the several constraints of the student and the learning material when selecting a sequence from repositories where several sequences could be chosen. Therefore, this has stimulated several researchers to use evolutionary approaches in the search for satisfactory solutions. This work explores the use of an adaptation of the prey-predator algorithm for the ACS problem. Pedagogical experiments with a real student dataset and convergence experiments with a synthetic dataset have shown that the proposed solution is suitable for the problem, although it is a solution not yet explored in the adaptive learning literature.
This study aimed to gain insights into the differences in perceptions of blog writing of two types of writers (i.e., digital natives [DNs] vs. digital immigrants [DIs]). The study focused on the generational literature and Web 2.0 as an online writing platform, investigating the generational differences in DN and DI writers' perceptions on a blog-based writing platform. The “WritingGen” blog was developed for this study to provide a web-based writing place to facilitate writers' writing and editing practices. An empirical study was conducted involving 34 Taiwanese blog writers with five hypotheses to be verified. Data were analyzed using independent samples t tests and logistic regression. The results revealed that the DN writers have significantly more positive attitudes toward blog writing, higher frequencies in blog-based writing behavior, perceived higher satisfaction, and higher knowledge acquisition than the DI writers. Based on these findings, pedagogical implications are provided.
From random interviews of mathematics teachers, the researchers are conscious that students have difficulties in solving problems regarding compound body volume measurement. The researchers found the main factor involved in the difficulties was incomplete spatial concepts. Augmented reality (AR), which is a kind of educational technology, has been widely applied in the educational field in recent years. AR provides two- or three-dimensional objects and/or information and interaction with them. These characteristics can compensate for the insufficient characterization of compound-body volume in traditional education environments. The paper studies evaluation in utilizing free augmented reality to learn volumetric measurement of compound bodies to complete spatial concepts as well as improve the students' learning performance. The finding suggests that the positive impact on visualization and interaction as well as attitude lead students to be more engaged in learning activities with less cognitive effort, resulting in better learning performance.
Studies on gender differences in mobile English learning are in their initial stages. College English IV, designed by a number of professors and engineers in a university in China, serves as a mobile English learning platform. This study aims to determine gender differences in cognitive loads, attitudes, and academic achievements in English language learning assisted with this mobile English learning platform. Through a mixed research design, 79 randomly selected participants join the research, together with interviews and posts to collect qualitative data. Cognitive loads, attitudes, and academic achievements are measured through related scales to collect quantitative data. It is concluded that there are significant gender differences in cognitive loads, attitudes, and academic achievements in English language learning assisted with the mobile learning platform. Future research on gender differences in mobile English learning should also examine relationships between gender differences, learner motivation, achievement goals, and learning outcomes.
There has been an ongoing debate of which physical labs or virtual labs are better. To resolve this issue, a remote lab provides an online lab that can do real experiments to obtain real data from a distant physical lab. Instead of relying on a remote lab, this article suggests that students collect experimental data locally with low-cost data loggers and then model the data with a web tool that provides scaffold support like a remote lab or virtual lab. In this study, 32 tenth-grade students ran physics labs and collected data with NXT, smartphones, and digital video recorder. This study investigates how a web tool assists in data visualization, hypothesis generation, hypothesis testing, and regulation of the discovery process. Results indicated the students became more sensitive in applying strategies of parameter tuning and backtracking. Questionnaire responses indicated the students found such physical labs to be satisfying.
The rapid growth of e-learning around the globe is inspiring various academic institutions to adopt it. Uptake is motivated by convincing benefits such as flexibility, accessibility and the management of course delivery. In fact, academic institutions place great emphasis on e-learning and are investing significantly in information technology infrastructures. However, in spite of this effort and investment, it seems that instructors and students do not always fully benefit from the learning technology and more often learning management systems (LMSs) remain underutilized. Thus, this study adopts the Unified Theory of Acceptance and Use of Technology (UTAUT) to study how people accept and use the Blackboard system. The data were analysed using Structural Equation Modelling (SEM) techniques to test the hypothesized research model. The empirical results found that technical support is fundamental in determining the acceptance and use of e-learning systems. The findings of the study may help to provide insights into a better approach to promote e-learning acceptance.
Discussion forums in learning management systems (LMS) have been shown to promote student interaction and contribute to the collaborative practice in the teaching-learning process. By evaluating the postings, teachers can identify students with learning difficulties. However, due to the large volume of posts that are generated on a daily basis in these environments, manual analysis becomes impractical. This article proposes a mechanism to support teaching through the thematic relevance analysis of the posts made by students in discussion forums. For this, text mining and metrics from network science were used to process and extract characteristics of the texts. Then, the processed texts were classified through supervised learning algorithms. The results show that the use of these techniques may generate potentially useful indicators for teachers to help them improve their pedagogical practices.
Augmented reality (AR) applications can be used in almost all education and training environments. In this study, it reveals the relationship between perceived usefulness, utility and attitudes regarding the use of AR applications in educational environments as well as the relationship between attitude levels and academic achievements. It also reveals the effect of AR application use on academic achievement in education. According to the findings obtained in the study, the followings have been found: the perceived ease of use of students regarding AR applications in educational environments has a strong positive effect on perceived benefit; the perceived benefit and ease of use influence the attitude levels strongly in the positive direction; there is no semantic relation between attitude levels and academic achievement; and the use of AR applications in educational processes increases the academic achievement of students.
E-learning plays a significant role in educating large number of students. In the delivery of e-learning material, automatic e-assessment has been applied only to some extent in the case of free response answers in highly technical diagrams in domains like software engineering, electronics, etc., where there is a great scope of imagination and wide variations in answers. Therefore, the automatic assessment of diagrammatic answers is a challenging task. In this article, algorithms that compute the syntactic and semantic similarities of nodes to fulfill the objective of automatic assessment of use-case diagrams are described. To illustrate the performance of these algorithms, students' use-case diagrams are matched with model use-case diagram. Results from 13,749 labels of 445 student answers based on 14 different scenarios are analyzed to provide quantitative and qualitative feedback. No comparable study has been reported by any other label matching algorithms before in the research literature.
The increasing use of the Learning Management Systems (LMSs) is making available an ever-growing, volume of data from interactions between teachers and students. This study aimed to develop a model capable of predicting students' academic performance based on indicators of their self-regulated behavior in LMSs. To accomplish this goal, the authors analyzed behavioral data from an LMS platform used in a public University for distance learning courses, collected during a period of seven years. With this data, they developed, evaluated, and compared predictive models using four algorithms: Decision Tree (CART), Logistic Regression, SVM, and Naïve Bayes. The Logistic Regression model yielded the best results in predicting students' academic performance, being able to do so with an accuracy rate of 0.893 and an area under the ROC curve of 0.9574. Finally, they conceived and implemented a dashboard-like interface intended to present the predictions in a user-friendly way to tutors and teachers, so they could use it as a tool to help monitor their students' learning process.
This article explores whether a learning community can affect students' learning achievement and engagement. Besides, this study also analyzed whether degree centralities of peer interaction affect learning achievement and learning engagement based on social network analysis. While the experimental group combined the English learning system with the online learning community, the control group was simply using the English learning system. The results indicated that the students' engagement from the online learning community were higher than the ones who used the English learning system only, although the learning achievement is not significant difference between these two groups. Moreover, higher interaction learners from the online learning community revealed better performance in learning achievement and student engagement. Other than that, the learners who played the “Center” emerged with a higher learning achievement as well as the students' engagement than the “Periphery” ones. The research provides suggestions for online learning with learning communications as well.
Extroversion and introversion are two of the personality variables mentioned in the context of learning and achievements. The present article examines the performance of students in a distance learning environment, focusing on the issue of the distinct effect of specific personality attributes (in this case, extroversion and introversion). The study included 171 respondents divided into three research groups. Each group received a different form of feedback – content feedback, effort feedback and ability feedback. Significant differences were found between the groups that received ability or effort feedback and the group that received only content feedback. Relationships were found between extroversion-introversion and the changes that occurred in motivation and achievements. It seems that extroverts benefited considerably from ability and effort feedback rather than content feedback.
With technological advances, distance education has been frequently discussed in recent years. The learning environments used in this course usually generates a great deal of data because of the large number of students and the various tasks involving their interaction. In order to facilitate the analysis of the data, the authors researched to identify how interaction and visualization techniques integrated with data mining algorithms can assist teachers in predicting students' performance in learning environments. The main goal of this work is to present the results of such research and the visual analysis approach that the authors developed in this context. This approach allows data gathering on the students' interactions and provides tools to investigate and predict pass/fail rates in the courses that are being analyzed. Our main contributions are: the visualization of the resources and their use by students; the possibility of making an individual analysis of students through interactive visualizations; and the ability to compare subjects in terms of students' performance.
This article focuses on comparing two e-learning strategies (cooperative versus collaborative) in terms of developing preservice teachers' skills in designing one of the modern Web 2.0 assessment tools (i.e., e-portfolio) and their effects on product quality and achievement motivation towards designing e-portfolio. After the experiment involving 80 students from the third level at the faculty of education, King Faisal University in Saudi Arabia, during the first semester of the school year 2017/2018, a questionnaire on achievement motivation and a product quality assessment card were used. The results show no significant difference between the two e-learning strategies regarding students' achievement motivation. On the other hand, there is a significant difference in product quality in favor of cooperative e-learning. The study highlights social-network based e-learning strategies of developing preservice teachers' teaching and evaluating skills that they need to apply in the new digital era.
This article compares two studies, investigating administrator, faculty, and student perceptions of quality in online/blended courses conducted in two different contexts, namely (1) two midsize public universities in the United States, and (2) a college in a public university in Malaysia. The research question explored in both studies was: What is the meaning of “quality” in an online/blended course to administrators, faculty, and students? Survey data from the three constituents in both contexts were obtained. Qualitative data analysis revealed the top 7-8 quality features of each context as ranked by number of references. The results revealed similarities and differences in the rankings of the quality features between constituents and between contexts. Similarities suggested that different constituents had different priorities with regards to quality features while differences appeared to be based on where each institution was on their distance education trajectory. These findings should be considered and reflected on in online course design, teaching strategies, and student support.