E-Learning Computer-Based Learning Packages - Literature review Example

LЕАRNING СОMРUTЕR BASED LЕАRNING РАСKАGЕS TО TЕАСH ЕNTITY RЕLАTIОN DIАGRАMS By Student’s Name Code + Name of Course Professor/Tutor Institution City/State Date Е-Learning Computer Based Learning Packages to Teach Entity Relation Diagrams Why Entity relation data modeling is hard to learn? Or hard to teach? Entity relation data modeling is used for theoretical data modeling in database designs. Development of superior ER data models is a challenging activity for junior modelers and learners. The major cause for this challenge is based on the premise that ER modeling tasks are cognitively complex (Ausubel 1963). Whereas the drawing notations offered by a method that seeks to model or structure the problem domain data necessities the transition process from the description of textual problem domain into the ERD model with notations mainly occurs based on the use of heuristics that are neither structured nor defined. Batra (2007) asserts that modeling of conceptual ER data is an activity performed in the course of the process of systems development in order to create a representation of the preferred semantics of a real-world challenge or an application domain. ER modeling is a task for problem solving, often considered as complex and dependent on critical thinking (Reiner 1992). It is often undertaken through the function of rules employed in notations and heuristics. Additionally to the subject on knowledge of the challenge in the domain, the shift step necessitates skills in problem solving. These entail several cognitive processes including visualization of abstraction and details, trial and error, and several basic rules of professionals like determination of the correct group of relationships, resolving many to many (M-M) relationships, and the proper determination of mandatory and optional semantics. The appliance of such cognitive steps in ER modeling is rather complicated, and therefore, generates in a cognitive challenge on the part of novices or learners. This is the explanation for the quality production of ER models, by teachers or learners, as well as the challenge of teaching ER modeling to new students. This requires a set of well thought-out notations in the place of user data needs (Lo & Choobineh 1998). However, it does not particularly offer rich and distinct heuristics knowledge to support the cognitive activities or processes involved in the formulation of the ER model semantics and constructs. In essence, the ER data modeling process requires five fundamental cognitive actions. The acts of identification of entities, attributes, and relationships requires the abstractions of heuristics, i.e., the classification, specialization, aggregation, and the generalizations of concepts in constructs like relationships and entities. This classification of constructs generates the oversimplification of the problem. These acts are commonly preceded by verification of the resultant ERD model in opposition to the associated user views and business rules described in the prerequisites. The verification entails visualization of links between instances of associated entities. The process of trial and error is applied during the class of identification of relationships or entity abstraction or between proposed relationships and entities, in order to attain the accurate ERD model section for every rules of the business (Mitrovic & Martin 2002). Therefore, the existence of these cognitive activities are challenging for novice modelers or learners since they lead to greater cognitive complexity (Eid 2012). Additionally, teaching students to perform these cognitive acts portrays immense challenges for instructors. Generally, novices make lesser errors during the recognition of entities than the detection of relationships stage. The most typical mistakes are the incorrect discovery of relations, determination of connectivity in the mandatory and optional semantics, as well as the incorrect resolve of cardinality semantics. Learning of knowledge and skills for the practice of ER modeling represents a cognitive challenge on the learner. Additionally, the teaching of the theoretical ER modeling portrays a challenging activity on the part of teachers (Eid 2012). E-Learning Vs. traditional Learning The growth of the Internet has incorporated fast development of online learning programs. Students from different institutions have access to several online options, which either supplement their classroom learning, or in other cases provide a complete degree program (Abo El-Soud, Hassan & Shohieb 2010). E-learning is advantageous because it is convenient for students who prefer online classes. Such students can visit a library or sit at home to undertake their coursework. This removes the requirement for driving enabling saving of money and other resources like gas. In other cases, students can attend classes from distant countries. Online learners can also develop their technology, virtual team, and Internet by taking part in online tests, discussions, and other online learning activities. The capability of students’ doing their coursework on their own schedules enables them to manage the speed of their learning experience, which benefits the e- learners (Al-Dahoud, Walkowiak & Woda 2008). Nevertheless, E-learning has some demerits. This is because a main factor in evaluating their potential for successful online studies is dependent on self-discipline levels. There are potentials for misconstruing that online classes are less time-consuming. In the real sense, they require more motivation, time management, and self-discipline to complete tasks. Limited access and interaction with instructors and lack of personal contact with course mates are common drawbacks. Whereas there are effective online instructors via web conferencing, phone and email, there is less interpersonal relationship in e-learning. The prospect for misunderstanding and miscommunication is more prevalent with e- learning particularly given the absence of nonverbal messages and context in discussions and email (Blackboard 2000). On the other hand, traditional learning entails common classroom learning. The advantage of this means is that classroom learning offers the multi-sensory impression. Students can relate with the instructor, receive illustrations through PowerPoint handouts, images, or whiteboard lists and undertake actively in hands-on actions, as well as case studies. Students have a directly access and interact with their course instructor. Therefore, students have chances of asking questions and participating in live discussions. Thus, they can benefit from firsthand experiences of other students’ accounts. Traditional learning provides opportunities for building and maintaining professional and personal relationships in classrooms, as well as personal contacts amongst students (Al-Dahoud, Walkowiak & Woda 2008). The disadvantage with classrooms is that in contrast to e-learning, traditional learning is usually fairly structured. Students meet at regular scheduled periods every week. This hinders flexibility with other activities. Students have to be in class to obtain the experience of learning and stay updated with requirements. Students who miss sessions miss out on lessons. Students struggling to focus may find traditional learning and cohorts disturbing to their learning experience. Different Systems of e-Learning Two types of e-Learning systems will be analyzed: Learning Management Systems and Learning Content Management Systems. Learning Management Systems (LMS): This system is generic software, which tracks and manages the learning processes of teachers and learners (Fallon & Brown 2003). It uses a web-based database, and therefore, utilizes the web as its portal for tracking and managing the activities of learning of learners. The platform offers and allows the administrative management, tracking, and delivery of blended and online learning. LMS is applied to operate and order the accessibility to learning materials, extent and handling of online content. The system also records all the activities of users together with their preferences that are later generated as statistical data on level of use (number of users, time spent by a given user, estimation tracking, activities of users and the tools used by the users). These give an apparent idea on what the users find interesting or dislike. With this information, there will be mechanisms developed for increasing or decreasing the usage of given courses, tools, and assessments. The data generated will indicate the effectiveness of e-learning courses portraying the significance of LMS in e-learning. LMS provides administrative, assessment, communication and collaboration tools. In the administrative tools instructors’ list new learners and users and if available, e-learners can register themselves. These tools assist in adding or dropping online courses for e-learners (Weerasinghe & Mitrovic 2002). In authorizing courses, this task is employed to offer definite access permits to course substance as well as apparatus based on given user responsibilities. Additionally, as section of the administrative instruments, it permits the instructors and administrator to authorize courses. LMS also offers communication and collaboration tools that assist users‟ collaboration, and interaction within or outside their classrooms by giving complete communication instruments for sustaining their learning for example file exchanges, notes, and video-conferencing. LMS targets the administrators and instructors and, training managers. Thus, LMS offers primary management to learners assisting them to report learning outcomes, and giving a means of collaborating, and gathering profile data of users (Clarke 2006). Learning Content Management Systems (LCMS) Naidu (2006, p. 18) describes an LCMS as a “multi-user environment” where instructors of learning can establish, store, recycle, administer, and convey digital knowledge content or objects from a middle repository. The necessity for LCMS was established with a huge amplification in the figure of LOs and the necessity to handle and reinstate the figures in a competent way (Clarke 2006). LCMS provides the ability to undertake a superior LOs search, developed content management, production and creation of distinctive definitions for every LO. LCMS also assists in delivering various levels for organizing and storing LO, enabling the collection of complex course structures. Conclusively, LCMS offers in-built authoring instruments for creating courseware objects for learning; an effective means of using content over different courses. The targeted users of LCMS are instructional designers, content developers, and project managers, who organize and collect the LO that is delivered and published in courses later on. LCMS offers learner collaboration, as well as the ability to organize reusable substance, use navigational directions to provide content, and establish administrative tests. LCMS can hold different types of learning tools like illustrations, images, HTML pages, PowerPoint slides, online modules, and video/sound clips. The capabilities and facilities provided by LCMS have popularized the system LCMS differ based on the needs of customers. There are different bodies and firms that add towards LCMS development (Fallon & Brown 2003). Computer Based Learning Packages, E-Learning Possible Solutions To Teach Entity Relation Data Modeling Currently, computer-aided software engineering (CASE) tools only provide drawing tools, which seek to improve the productivity of modelers during theoretical modeling. Sometime back, several knowledge-based E- learning tools that support ER modeling were proposed. Nevertheless, the efficacy of such tools is unknown as they have never been evaluated by experimental testing (Elmasri & Navathe 2007). However, certain modern tools strengthen e-Learning conceptual data modeling. Several computer-based packages are used for learning and provide solutions for teaching ER data modeling, as well as knowledge on UML diagrams. The suggested UML surroundings can automatically accurate class diagrams training giving direct response to students. Computer packages can use database courses for entity-relationship diagrams in instructions on conceptual modeling. E-Learning is essential in supporting and teaching ER data modeling via the application of ICT. E-Learning via the use of computer-based ICT is claimed as a computer-based ICT used by teachers and students as a mean a way of describing and exploring the activities which occur in ER data modeling contexts. In this sense, this modeling exists at a high level of abstraction is more explicit in theoretical learning commitments like cybernetic, cognitive, or socio-cultural (Carte & Ingid 2006). Computer based learning packages in the e-learning offer an advantage in line with the entity-connection model (ER) because it is largely comprehended in the computing group, while ER is incomplete within the database population. In e-Learning, computer packages are effective for ER modeling. Conversely, it is unusual to find UML class diagrams being employed as the basic detail in teaching information modeling. E-learning requires that software applications to be used in drawing prospects on a similar basis as the instructors one works with. Nevertheless, it is significant to uphold a philosophical differentiation between the models used in E- learning. The computer packages assist learners to provide frameworks through which ER data models can be analyzed especially in line with the experiences of the learners. They offer a high order level for description the ER models by highlighting the overall description of the learning field. They can show where new learning models have to be created. In other cases, changes in the theoretical perspectives of behaviorism towards cognitive, as well as from people towards development of social ideas has necessitated the development of new data modeling methods. Despite the capabilities of computer based packages within the educational environment, it is expected that the e-learning apparatus offer the functionalities that led to their designing as well as functionalities associated with academic issues like tracking student work, assessment etc (Boada, Soler & Poch 2004). Cognitive Methods for Learning and E- Learning Systems The suggested learning scheme for ER modeling is founded on a cognitive structure of learning and resolving problems. It employs animation and graphical organizers to minimize cognitive challenges during ER modeling succeeding the cognitive load hypothesis (Chen 1976, p. 9). Additionally, the constructivism view of learning is functional in this case, as the student assumes more control of his or her learning through amplified practice of modeling several problem domains. Given that the activity of ER modeling largely depends on graphical representation of modeling constructs, in the suggested system, the graphical user interface diagrams and forms are created by basing on the graphical organizer method (Sweller 1988). They are created in a manner that simplifies the complexity of the problem domain, while offering focus and guidance throughout the process of modeling. They support trial and error as well as the visualization of details and hence support abstraction processes of thinking. The process implants basic expertise rules used in establishing the right class of relationships, solving M-M relationships as well as correctly determining of the mandatory and optional semantics. Therefore, the cognitive methods of the learner will be improved during ER modeling, and therefore learning will improve due to the experience with the tasks engaged in of ER modeling. The proposed learning system is founded on systematic framework, which compels the learner to undertake major measures of ER modeling. The suggested learning framework will quickly establish the self-efficacy of the student in ER modeling. To define the functions of the system, a systematic and generic process for ER modeling has to be created. This should base on cognitive abilities of creating and teaching ER models together with the conceptual ER data modeling. The cognitive methods for establishing a learning or ER learning system should entail the creation of domains for describing new problems based on the generated requirements of IS application. This also defines and identifies relationships, semantics, and entities like the optional and mandatory. Furthermore, it should define the attributes of the entities in learning (Ryan, Bordoloi, & Harrison 2000). How A Computer Based Learning Package Such As The Link Bellow Can Help Student To Understand Entity Relation Diagram? Computer based learning packages are essential in assisting the comprehension of the association between students’ styles of learning styles as well as their preferences for strategies for instructions in ER models (Saeed, Yun, & Suku 2009). The package of using rising web innovations can assist in suggesting learning styles that offer information about personal differences in learning. These are essential in proposing ways of designing instructional preference. This entails incorporating emerging web technologies into the ER modeling data of students’ styles of learning as well as the technology preferences, which should be considered in order to successfully integrate emerging web technologies into their courses. The current development in applications of e-learning systems in the learning system is a fundamental change that can assist in comprehension of ER diagrams of students (Saeed, Yun & Suku 2009). This shift in learning from the traditional classrooms to environments that are web-based has improved the challenges experienced in ER diagrams. This enables students to learn in many different ways. Therefore, these students can focus on algorithms, facts, and data; others prefer mathematical and theoretical models. Some learn better from visual information like simulations, pictures, and diagrams from the web while others prefer interactive learning. Students and instructors that use of effective technology completely manipulate their academic excellence. In order to comprehend ER diagrams, individuals are adopting different levels of emerging web technologies within academic settings. Other technologies comprise wikis, podcasts, IMs, vodcasts, blogs, social bookmarks etc. (Saeed, Yun, & Suku 2009). A Blog is a kind of website, commonly upheld by a person with usual entries of explanation of actions, or substance like graphics are emerging Web technologies with the potential of creating engaging learning environments and understanding ER models. In academic set-ups, students and instructors attain many benefits of such interactions. For instance, blogs aid the publication of opportunities for knowledge for subsequent analysis. As ER models, they aid learners comprehend the contextual and relational basis of knowledge, construction and meaning of knowledge. Social bookmarking assists students to store, search, manage and organize bookmarks of internet pages typically in the form of tags. Understanding the association among instructional strategies and learning styles holds room for enhancing the perceptions of learners of their own learning. The importance of knowing the learning styles of students is important in designing and managing different web-based situations or other materials for learning in different subjects relating to ER. This makes current learners more flexible in expanding their learning styles and in the process accommodating different varieties of teaching methods. ER learning can be understood within varied learning styles that reveal meaningful relationships between verbal and reflective and the global or intuitive learners. This revelation is important in coming up with significant web aids that have an impact in forming effective groupings in ER modeling programming projects or assignments. The associations within a variety of technology preferences shows that students prefer using both asynchronous and synchronous communication instruments within their academic communication. These are useful in the process of systems development in order to develop a representation of preferred semantics of a real-world challenge or application domains. This implies that learners are prepared to practice emerging innovations within their studies and are eager to work together with numerous channels of communication to improve their comprehension of ER models. The correlation amid technological preferences and learning styles reveals interesting outcomes. There are learners who prefer emails than other applications. There are also those who are more comfortable with particulars and are thus more careful. Additionally, there are those who totally rely on traditional tools of communication than other students. The association between podcasts and visual learners is natural which enables learners applying ER models to learn from demonstrations, flow charts, diagrams, and pictures (Saeed, Yun, & Suku 2009). Human Computer Interface Design: Conclusions reached for the Link There has been considerable interaction of humans and computers in e-learning. This platform has established an increase in the figure of virtual universities globally (Al-Khalidi 2014). A worldwide education system, as a main area of the prospects of ICT, has made developers to come up with several low cost systems of learning. The expanding number of universities, together with a huge number of internet users has provided immense benefits to students all over the world. The systems development model proposed by developers towards the environment of e-learning in most universities is a significant step in the modern learning environment. There have been various suggestions as well as possible solutions for implementing methods of e-learning in effective ways (Al-Khalidi 2014). Several models of web-based services of e-learning describes all the essential functionalities for the interactions between learners and learning centers. However, actual development in terms of establishing new styles of learning based on quality learning can be analyzed only over a long period. One of the main merits of e-learning is convenience. The venture permits students to learn and work at their own speed without time restrictions linked with traditional methods of learning. Given that e-learning offers access to learning resources at any time, students have the ability of arranging their learning around jobs, as well as other activities. This is particularly an important thought for students intending to study in a distant country. The likelihood of engaging in online dialogues is also an advantage for many people (Al-Khalidi 2014). Nevertheless, most e-learning systems in many countries are unfavorable rated by student users because they lack systematic evaluation as well as the offered courses having out-of-date resources. This dissatisfaction amongst students does not arise from lacking available technology. The fact is most learners are content with the existing online resources. Attempts to improve student contact, together with the requirements of learners to finalize compulsory mutual e-learning activities, are therefore capable of enhancing the e-learning experiences of students. There are many challenges of providing a quality experience online in organizations that are still determining approaches to e-learning. Therefore, modernization and implementation of sustainable quality e-learning programs must be ingredients of systematic incorporation of technology into the processes of learning in institutions of higher learning (Al-Khalidi 2014). E-Learning Evaluation: from Learning Theories Learning theories deal with how people learn. Similarly, the implementation of e-learning necessitates a clear comprehension of the way e-learners study learning theories through the behavioral, cognitive and humanistic, affective and social learning theories. These theories sustain differing Instructional Design models or Instructional Systems Design which majorly focus on exploiting the competence and worth of instruction, by mirroring on the knowledge experience through formative state of learners as well as establishing the goals of instruction. Behavioral Theory and e- Learning According to Gillani (2003), the use of behaviorism for the growth of e-learning has taken place since behaviorism was applied to technology, generating in an instructional replica called “Practice and Drill”. This method was projected as a suitable model of instruction for teaching Math through splitting the topic into divisions that were in turn were prearranged into skill goals. These goals are then delivered in the form of exercises. “Practice and Drill” is a means whereby a computer manages and controls the management of learning resources used to establish skills, together with structured practice, and trails the actions of the users in order to offer appropriate report. Medsker and Holdsworth (2001), suggest that the technique will function well in some subjects because learners contribute actively, through properly defined sequences, instead of just viewing the substance, and in scenarios where control by computers is desired. Furthermore, Patton (1990) considers that a behavioral approach can provide good e-learning environments in particular situations. Cognitive Theories and e- Learning According to Gillani (2003), cognitive development entails the mental process, which involve knowledge acquisition, storage, and retrieval to resolve challenges. In e-learning designs, the cognitive model influences the progress of instructive simulations, intellectual tutoring systems as well as graphical user interfaces (Medsker and Holdsworth 2001). Furthermore, it entails the inference of such things into e-learning. Constructivism Theory and E- learning In constructivism, the student is the major focus as opposed to the taught matter. This theory evaluates the interaction of E- learning with instructional material in the e-learning setting. This interaction determines the kinds of interaction, which occur in the e-learning environment like budget, the techniques being created, the instructional needs of the material technology and bandwidth. 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Saeed, N, Yun, Y & Suku, S 2009, ‘Emerging Web Technologies in Higher Education: A Case of Incorporating Blogs, Podcasts and Social Bookmarks in a Web Programming Course based on Students' Learning Styles and Technology Preferences.’ Journal of Educational Technology & Society, vol. 12, no. 4, pp. 98–109. Sweller, J 1988, ‘Cognitive load during problem solving: Effects on learning’, Cognitive Science vol. 12 no. 2, pp. 257–285. Weerasinghe, K & Mitrovic, A 2002, ‘Enhancing learning through self-explanation. Kinshuk, R. Lewis, K. Akahori, R. Kemp, T. Okamoto, L. Henderson, C-H Lee (eds)’ Journal of Science, vol.16, no. 4. pp. 244-248. Read More