Computer-Based Learning

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Self Efficacy in Computer Based Learning Environments: A Bibliometric Analysis

Self Efficacy in Computer Based Learning Environments: A Bibliometric Analysis

To that end, three databases were chosen, the first two of an international nature, Science Direct and Scopus, and the third of a regional nature and free to access, SciE- LO. Subsequently, a set of search words was determined in order to obtain a more pre- cise and representative sample of the published papers. The expressions used were “self-efficacy”, “computer”, “computer-based learning”, “academic self-efficacy”, and “Internet-based learning” for the Science Direct and Scopus databases, and the expres- sions “ autoeficacia [self-efficacy]”, “ autoeficacia académica [academic self-efficacy]”, “ aprendizaje en línea [onlined learning]”, “ enseñanza asistida por computador [com- puter-aided learning]”, “ educación a distancia [distance learning]”, and “Internet”, for SciELO. These expressions were established for the fields of: Title, abstract, and key- words, using as a connector the logic operator “AND”. The search was limited to pub- lication dates between the years 2006 and 2015. Additionally, the search was filtered by refereed and published; books, conference papers, dissertations, and other type of documents were not taken into account.
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The Brachial Plexus: Development and Assessment of a Computer Based Learning Tool

The Brachial Plexus: Development and Assessment of a Computer Based Learning Tool

The numbers of faculty who are qualified to teach gross anatomy at the professional level are pro- portionally shrinking every year when considering the ever increasing ratio of research oriented Ph.D.’s awarded and available academic positions to the steadily increasing number of students. 1 The prob- lem involving the lack of anatomy instructors is ex- acerbated by the fact that most life science Ph.D. re- cipients, including those from anatomy departments, can spend up to an average of five years in postdoc- toral positions learning research techniques while

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Utilising computer based learning to complement class teaching of gross anatomy

Utilising computer based learning to complement class teaching of gross anatomy

While the delivery of intensive short courses at higher education is not new, there is little research validating its effectiveness or otherwise. While there is a reasonable volume of literature dealing with short or intensive courses per se there is little known about its impact at tertiary level. Time-compressed delivery of units certainly have their advantages for both Universities as well as students, hence their frequent use, but their growing application has been mainly limited to management schools (Davies, 2006). Very few or a complete lack of their routine application is found elsewhere in the Australian tertiary sector with the exception of the US, where the educational sector is arguably different. Existing research into this area of pedagogy has focused mainly on student perceptions towards a single intensive unit (Grant David, 2001; Kasworm, 2001) from which data was collected immediately following the completion of a unit, and thus may not reflect any long term trends of such teaching method (Daniel, 2000). In previous research, the assessment of a single unit at a single point in time also fails to capture any information on student preferences and learning styles. In-spite of this lack of empirical data comparing alternative delivery formats, intensive teaching formats have been reported to be more popular with students for certain units (Grant David, 2001; Scott, 2003). One noted aspect within our block model are the consistently high attendance rates for most units. This means that students should ideally be provided with a learning environment or activity that capture the benefits and dynamics existing within such environment. To this end, we attempted to harness the time allocated to each workshop to capture greater student engagement and student-centered learning and to develop a culture of community learning through group discussion so important in staying connected.
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Evaluation of Computer-based Simulation Learning on Knowledge, Learning Approaches and Motivation among Pharmacy Students

Evaluation of Computer-based Simulation Learning on Knowledge, Learning Approaches and Motivation among Pharmacy Students

Background: With the exponential influence of technology on students’ learning, Computer-based Simulation Learning (CSL) has perceived to have great potential in enhancing the training of healthcare professionals including pharmacists. However, limited evidence is available to compare its impacts on knowledge gained and learning approaches to that of conventional lectures. Materials and Methods: A total of 168 pharmacy students were randomly assigned to “CSL group” (Group I, intervention group) or “Lecture group” (Group II) in this study. The students’ knowledge and deep information processing capabilities were evaluated through a quantitative survey, followed by focus group interviews to obtain an in-depth perspective of the student learning through CSL. Results and Discussion: Compared to didactic lecture, CSL significantly enhanced knowledge gained by the students. There was no significant difference between CSL and didactic lecture on students’ deep information processing skills, although CSL was more effective at promoting the critical reading domain. On the other hand, didactic lecture had higher impacts on healthcare students’ structuring skills. The features to be considered in designing an effective computer-based learning tool were highlighted in this study. Conclusion: Both CSL and didactic lecturing have its role in training pharmacy students’ knowledge and deep information processing skills.
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Cultural Difference and its influence on learning with Computer-based technologies in Schools

Cultural Difference and its influence on learning with Computer-based technologies in Schools

Apart from Henderson, other theorists have also highlighted the shortcomings of design models and guidelines. Their key criticism has been that most of these models tend to be of a general nature and provide guiding principles for designing resources for students in general rather than for specific target groups like cultural minorities. Stanton (2001) for example points out that there are many difficulties being encountered by designers of computer based learning environments. "Research evidence suggests that the present status of knowledge is at best somewhat wanting and at worst contradictory and unhelpful" (Stanton 2001, 181). He further points out that development in design of learning environments are “only likely to occur through empirical research into instructional theory and a better understanding of how people use CBLE". (Stanton, 2000:176)
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Learning IS child's play: Game-based learning in computer science education

Learning IS child's play: Game-based learning in computer science education

Engaging students directly in the process of learning is one of the most fundamental approaches to achieve mastery in the learning process. Active learning methodologies have been celebrated in the past few decades as means of improving cognitive abilities and promoting deep learning through effective participatory engagement. Meyers and Jones (1993) describe active learning approaches as those that provide students the opportunity to discuss, interact and reflect on the content, ideas and issues of a subject [28]. Bonwell and Eison (1991) enlist a variety of teaching methods that promote active learning including peer-teaching, computer-based learning, cooperative learning, and games [3]. Since then, numerous experimental studies have proved the effectiveness of active leaning methods in engaging students and promoting mastery over their traditional counterparts in various diciplines and levels of education [1, 15, 23, 32].
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Indexed Learning: Protagoras’s Computer

Indexed Learning: Protagoras’s Computer

Knowledge is information gained through introspection, ex- perience or learning. It is linked to social praxis and action (Habermas, 1985). Knowledge is sensitive in assessing the pos- session of multiple interconnected data that alone have low- quality but together have high-quality. Reasoning is the capac- ity of treating those data methodically, that is, the ability of spontaneously combining two or more separate or isolated ex- periences to form a new concept or idea which can efficiently serve for obtaining an aim. All these capabilities are character- istics of Homo sapiens, as Linnaeus classified the human spe- cies in 1758 in “System of Nature” (Paterlini, 2007). If we fo- cus on human capacities, it is absurd to state that everything has been done in education. In the area where education is essential, the school, now the capacities are called competencies: know- how in specific situations that require creative flexible and responsible application of knowledge, skills and attitudes (Bow- den & Marton, 2004). Teachers, especially in the Infant and Primary levels when students they are highly motivated and inspired by their ingenuity and creativity, expand the idea that participation and learning are inseparable (Hrastinski, 2009). On this methodological framework, they teach core compe- tences far away from the discursive methods of teaching and the textbooks as basic materials. With such innovative enthuse- asm they propose educational projects where the network (In- ternet) acts as a container of knowledge that facilitates learning (Groff & Mouza, 2008).
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Computer Support for Vicarious Learning

Computer Support for Vicarious Learning

Why focus on individual implementations of CSVL? Vicarious learning can be seen to work in professional productions. Examples on television include Open University education programs containing brainstorming and debates between lecturers and students and public debates often discussing issues concerning current affairs. However, a lecturer who wants to use such programs can face several obstacles. Firstly, the content of the program may not suit the need of the students/courses that the lecturer is teaching. Secondly, the materials are not grounded or linked to the students’ own experience and thus does not make use of the benefits of episodic memory. Such professional productions are also costly and require large amount of time and effort from the lecturer if he or she wants to produce similar material him/herself. For this reason, this research is asking whether computer support can bring the benefits of vicarious learning to individual lecturers. If so, how can it be done? If not, what are the main obstacles?
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Machine Learning in Computer Forensics (and the Lessons Learned from Machine Learning in Computer Security)

Machine Learning in Computer Forensics (and the Lessons Learned from Machine Learning in Computer Security)

With respect to a computer security tool, we identify three key requirements: it should be able to work in real-time, it should not generate too many false alarms and should be as autonomous as possible. The first one descends from the fact that typically the tool should be able to prevent the attack before it occurs. Anti-virus softwares, Intrusion Prevention Systems, or even Web Application Firewalls cer- tainly represent examples of tools that are required to detect the malicious patterns in real time. This represents a par- ticularly severe constraint, especially when large volumes of data must be analyzed (such as for instance in the case of a network-based Intrusion Prevention System). A second fun- damental requirement is related to the false alarms rate, that indeed must be low. This always represent a crucial point in the case of anomaly-based systems since this re- quirement has to be meet without affecting the generaliza- tion capability of the system. Finally, it is also desirable for the tool to be as autonomous as possible, requiring (possibly) no intervention by the user (at least if no attack occur).
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Deep Learning: A Vision for Computer

Deep Learning: A Vision for Computer

The information process of human consciousness and thinking can be imitated by using AI. In actual, it is not human intelligence, but it thinks like a human. Machine Learning is the type of AI and is directly associated to computational statistics. It bears the responsibility of taking using computers. ML is comprised of mathematical optimization which assists in delivering the methods, applications and theory in certain domain to the industry. ML can be referred to as the amalgamation with data mining [14], however, the other subfields focus on investigative data analysis and is called to be unsupervised learning. Machine learning can be unsupervised which is furthermore used to study and develop profiles with baseline behavioral for a variety of entities and eventually, these profiles are used to locate meaningful anomalies [15].
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Learning vocabulary in a foreign language: A computer software based model attempt

Learning vocabulary in a foreign language: A computer software based model attempt

Due to advances in educational technology and increased global contact, academic communities have been affected considerably which inevitably had a great effect on language teaching and learning (Warschauer, 2000). As a result of the impact of these changes in language teaching, teachers are becoming more familiar with terms such as education technology, science and technology, the Internet, hypermedia, multimedia, satellites, simulation, educational games, electronic networks, new methods of generation and transmission of visual and graphic information, virtual library, CALL and computer sciences applied to education (Hubbard & Levy, 2006). Although the primary activity remained to be called CALL, acronyms such as CALT (Computer-assisted language testing), and CASLR (Computer- assisted second language research) were created to cater for the growing interest and need for specialization in the field. Computer-assisted second language research (CASLR) tasks require learners to work on the target language interactively with a computer program or with other people through the medium of the computer. CASLR tasks may appear to the learners to be a regular part of instruction or assessment, or they may be introduced to learners as research tasks (Chapelle, 2001).
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CAPBLAT: an innovative computer-assisted assessment tool for problem based learning

CAPBLAT: an innovative computer-assisted assessment tool for problem based learning

This paper presents the development and implementation of Computer-Assisted Assessments (CAA) for helping lecturer in assessing students’ achievements on the Problem-Based Learning (PBL) Approach. PBL Assessment is typically formative; it includes delivery achievement feedback for the student, with the aim of improving their skills. The use of CAA in PBL gives advantages for both of lecturer and students by providing them with detailed formative feedback on their learning achievements compare to conventional assessment. It also reduces lecturer’s tedious load by automating parts of the task of marking students’ work. The methodology applied to this research was literature review and investigation of practitioners’ perception about assessment in PBL. The literatures showed that there are methods of assessments that have been used successfully in PBL, but the research selected seven methods as a PBL assessment framework; there are “Peer-assessment, Self-assessment, Group-presentation, Individual- activities, Group-report assessment, pre-test and post-test assessment." The framework applied to the tool that encompasses the use of computer (called with CAPBLAT) for helping lecturer in assessing students’ achievements. In addition, the CAPBLAT helps to store assessment material, deliver assessment, and does an auto-rating of the assessment result.
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Problem-Based Learning as an Effective Tool for Teaching Computer Network Design

Problem-Based Learning as an Effective Tool for Teaching Computer Network Design

A total of three PBL sessions were arranged within the module, with each having its own trigger, but also with each session building on the previous one, in effect, remaining in synchronism with the lecture thread, as shown in Fig. 2. The first trigger presented the students with a scenario in which their client (P&L Interior Designs) had recently acquired new business premises in Manchester, U.K., and required a LAN design for this building. A brief history of P&L Interior De- signs was given in which it was explained that the company had its headquarters based in Cheltenham, U.K., and had al- ready established one regional office in Greenwich, U.K. PBL session 1 came at a point within the module at which basic LAN operation had been covered. However, the PBL session purely described the operation of the client’s business and what functions it intended to carry out at its new premises. In addition, a detailed description of the new business premises was made available. Hence, the key point of this first PBL session was to encourage the students to use their technical knowledge, acquired from the lecture thread, and apply it to producing an outline design that would form the basis of a first meeting between the network design consultants (the students) and the client. This outline design comprised a basic network architecture but did not identify a precise component list. As a result, students learned how to determine a client’s networking needs from a knowledge of their business operation and pro- cesses, to decide which technologies are best suited to meet these requirements and to understand how physical restrictions, such as those imposed by a building, can also influence network design.
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Flexible learning in computer science

Flexible learning in computer science

Interactive tutorials and learning resources (reusable learning objects) can allow students to experience material in ways that can be learnt at a pace that suits them, with the opportunity to repeat content. Suitable progress assessments can assist in personalising this to ensure they have a sufficient grasp and evidence of engagement. Being web based, this clearly enables choice in where learning happens. Potentially, having a range of resources covering a particular topic with different learning styles can enable choice in the mode. Simply recording lectures can allow review of a session, and allow students who are not there to see it in their own time, but the choices are more limited ones, since there is no opportunity for interaction, or to ask live questions, nor for a variety of delivery approaches to support different modes of learning. Of course, a recorded lecture can be delivered and supported with a real time opportunity for Q&A, or asynchronous forums. This example illustrates how different forms of teaching can be positioned in the 3-dimensions of flexibility.
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ITS for Learning computer networks

ITS for Learning computer networks

Intelligent Tutoring Systems (ITS) has a wide influence on the exchange rate, education, health, training, and educational programs. In this paper we describe an intelligent tutoring system that helps student study computer networks. The current ITS provides intelligent presentation of educational content appropriate for students, such as the degree of knowledge, the desired level of detail, assessment, student level, and familiarity with the subject. Our Intelligent tutoring system was developed using ITSB authoring tool for building ITS. A preliminary evaluation of the ITS was done by a group of students and teachers. The results were acceptable.
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Unlimited learning Computer and video games in the learning landscape

Unlimited learning Computer and video games in the learning landscape

Bespoke interactive learning products Educational game products promote learning via interactive resources which are explicitly subject or curriculum-focused. They tend to be created by educational games/software developers from scratch, using tools and resources specifically designed for hardware already or easily available in classroom settings. Historically, educational games titles have centred on drill-and-skill models, particularly in mathematics and science. The relationship between the informational component and the game-entertainment has been explicitly divided, an aspect most contemporary developers of interactive learning software aim to avoid. The two most successful alternatives to the drill- and-skill paradigm have been simulations and adventure games which challenge users to take on the role of detective in order to problem- solve and explore.
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Weighted transfer learning for improving motor imagery-based brain-computer interface

Weighted transfer learning for improving motor imagery-based brain-computer interface

Despite several recent advances, most of the MI-based BCI applications are still limited to the laboratory due to their long calibration time. As the literature shows [6]–[8], due to considerable inter-subject and inter-session variations, a reliable machine learning model that performs well across all sessions and subjects has not been feasible yet. Consequently, a 20-30 minutes calibration phase at the beginning of each new session is typically conducted to acquire sufficient labeled data to train the subject-specific BCI model. This calibration phase is time consuming and fatiguing, leaving a reduced amount of time for real BCI interactions [9]. Thus, developing reliable methods and approaches that reduce calibration time while keeping accuracy in an acceptable range is highly desirable in MI-based BCI research [7], [9], [10].
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Problem-Based Learning Environment In Basic Computer Course: Pre-Service Teachers Achievement And Key Factors For Learning

Problem-Based Learning Environment In Basic Computer Course: Pre-Service Teachers Achievement And Key Factors For Learning

roblem-based learning (PBL) is an important practice that provides suitable learning environments for learners, in which they acquire complex problem-solving skills (PSS) by working with real-life problem situations. In today’s world, learners’ PSS and collaborative working skills are vital qualifications (Dochy, Segers, Van den Bossche, & Gijbels, 2003; Engel, 1997; Gijbels, Dochy, Van den Bossche, & Segers, 2005; Loyens, Gijbels, Coertjens, & Côté, 2012; Spronken-Smith & Harland, 2009). Moreover, especially in higher education, learners take courses with the aim of gaining profession-related skills (Loyens et al., 2013; Thomas, 2009). The PBL method originally was developed for medical education at McMaster University in Canada in the late 1960s (Barrows, 1994; Gijbel et al., 2005; Parton & Bailey, 2008). Furthermore, according to Schmidt (1993), PBL is compatible with John Dewey’s empiricism and Jerome Brunner’s discovery learning approaches, which are the basic ideas of constructivism. In this respect, PBL shares similar characteristics with the constructivist approach (Stefanou et al., 2013). Barrows (1994, 1996), Gijbel et al. (2005), and Reynolds and Hancock (2010) describe the following six PBL characteristics. (1) Learning is student-centered: students should take responsibility for learning and also actively engage in the learning process through real experiences, meaning that they are able to use all their senses for learning. (2) Learning occurs in small student groups: a small student group should have five to nine members. In the group, students can perform in-depth problem solving with different group member perspectives. (3) Teachers act as facilitators or student guides: at McMaster University, the teacher’s facilitator role is much like that of a tutor (Barrows, 1996). (4) Problems form the organizing focus and stimulus for learning: The problem is presented to students in different ways, such as written papers, computer simulations, video or audio recorders. (5) Problems are vehicles for the development of problem-solving skills: Learners work on the problems and make decisions to solve them. Moreover, they gain experience and learn different strategic approaches related to the main question of how the problem can be solved. (6) New information is acquired through self-directed learning: Learners regulate their problem-solving strategies and solutions based on their own experiences.
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SUPPORT OF COMPUTER-AIDED LEARNING SYSTEMS IN DISTANCE LEARNING

SUPPORT OF COMPUTER-AIDED LEARNING SYSTEMS IN DISTANCE LEARNING

Computer-Aided Learning Systems provide their support in many different areas. One of the areas in which computer-aided learning systems have a great potential is that of distance learning. Distance learning is a kind of learning when there is no face to face contact with the teacher and teaching is delivered to the students who are not physically present in a class room. It provides learning when there is time and distance separation between the learners and source of information. The distance learning allows to learn around work and home life. This work presents the support of computer-aided learning systems in the field of distance learning.
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Detection of pulmonary ground-glass opacity based on deep learning computer artificial intelligence

Detection of pulmonary ground-glass opacity based on deep learning computer artificial intelligence

With regard to the utility of deep learning for the detection and classification of pul- monary nodules and GGOs, we did find that the method of deep learning proposed in our study is feasible; however, there are still some shortcomings and improve- ments will need to be made. First, compared with other CAD system experiments, the FPs in our study are a little high; we intend to try to reduce it by introducing the pre-trained deep neural network algorithm weights in ImageNet data to initialize. Second, deep learning requires training data with large sample sizes. The samples of pulmonary ground-glass opacity used in our study are still not adequate. We will con- tinue the experiment with regard to GGOs, with a higher sample size, and we believe that the accuracy of deep learning could improve further. We also plan to collect the pathological results of GGOs and use them in deep learning. We hope that in addi- tion to identifying pulmonary nodules correctly, deep learning can provide a prelimi- nary diagnosis of nodules as benign or malignant in order to make the results of CAD more applicable in clinical practice.
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