Effects of Collaborative Web Based Vocational Education and Training (VET) on Learning Outcomes

Considering the increase in need of skilled human capital, the vocational training and education through e-learning has widely been adopted all over the world. It serves as a major alternative for training and teaching professionals, technicians, skilled workers, amateurs and students who cannot manage regular college education due to time, cost and distance factors. Prior research focuses on learning enhancement from several perspectives like computer mediated methods, learning games, personalized learning environments and web based instructional design. The proposed work contributes to this debate by investigating the role of collaboration i.e. student–student and student–instructor interaction, support material, instructor's feedback and evaluating their effects on learning outcome and students' learning experience. The investigation is performed through a case study presenting four vocational courses offered in three consecutive sessions. The findings revealed that collaborative practices i.e. group work, team effort, in time instructor's feedback and consolidated support material enhances learning experience of student and contributes positively to the learning outcome.     

The student experience of a collaborative e-learning university module

The aim of this paper is to present a picture of student experience of a collaborative e-learning module in an asynchronous e-learning environment. A distance learning module on music education worth five credit points for a bachelor online degree for primary school educating teachers was assessed using a self-evaluation questionnaire that gathered quantitative and qualitative data about student satisfaction of the collaborative e-learning activity. The quantitative part of the questionnaire consisted of 27 closed questions on a 10-point Likert scale and offered data about satisfaction with the module. The qualitative part of the questionnaire provided an insight into the participant perspective of the online collaborative experience. General open questions on satisfaction and dissatisfaction were analyzed with an inductive analysis which showed the evaluation criteria used by 92 students. Results of the analysis showed five themes of the participants’ perspectives, which were interpreted by the researcher as: teamwork, cognitive, operating, organizing, and emotive/ethic for the positive aspects and teamwork, operating, organizing, and emotive/ethic for the aspects to be improved. The aspects that were associated with satisfaction include: collaborating, comparing ideas, sharing knowledge and skills to support each other, peer learning, analyzing and integrating different points of view, the usability of the platform, group planning and workload management. Aspects of the student learning experience that should inform the improvements of e-learning include: more collaboration between students since some students engage differently; more coordination and organization, the workload management in the group activities, some technical problems such as updating modifications. The participants’ results in the module increased their didactic potential as primary school teachers. The findings are discussed in relation to their potential impact on developing collaborative activities addressed to teacher education in distance learning. Implications for future research are also considered.     

Integration into mathematics classrooms of an adaptive and intelligent individualized e-learning environment: Implementation and evaluation of UZWEBMAT

The purpose of this study is to design an adaptive and intelligent individualized e-learning environment based on learning style and expert system named UZWEBMAT and to evaluate its effects on students’ learning of the unit of probability. In the study, initially, learning objects were prepared in three different ways in relation to Visual–Auditory–Kinesthetic (VAK) learning style for each subject of the probability unit. These were appropriate for secondary school mathematics curricula. Then, they were transferred into the digital environment. Each student may follow a different course, and the solution supports s/he will get may also differ highlighting the individual learning. The sample of the study consists of 81 10th grade students from two high schools in Trabzon, Turkey. Qualitative and quantitative data were collected from students to answer research questions. Quantitative data were given as frequency distribution and percentages. Qualitative data were analyzed using qualitative data analysis methods. Results of the study indicated that opinions regarding UZWEBMAT are rather positive. Aiming at individual learning, UZWEBMAT provides the most appropriate environment for students. In addition, UZWEBMAT can be used as well to reinforce traditional classroom education.     

Temporal support in the identification of e-learning efficacy: an example of object classification in the presence of ignorance

Abstract: This study investigates the efficacy of an in-place e-learning facility, towards the performance of students on a university module, through the utilization of an information system based on CaRBS (Classification and Ranking Belief Simplex). It attempts to discern the final indifferent and good performance classifications of students based on their activity during the module (online page views). The ongoing assessment of the students every two weeks during the module is modelled with separate classification results reported as the number of two-week periods builds up. Students who completed the course as well as those who withdrew are considered. The CaRBS system has its emphasis on the visual representation of results (using simplex plots), as well as its operations in the presence of ignorance (using the Dempster–Shafer theory of evidence). In the analysis of the module considered, the activity over certain weeks is consistently shown to be important to discern student performance. This study is of interest to practitioners and theorists, with the results shown offering a benchmark to the type of findings pertinent to expositing e-learning efficacy.     

Learning style as a factor which affects the quality of e-learning

With the aid of the Internet, many organizations and schools have adopted the idea of applying the e-learning system, which is considered as one of the most important services provided by the Internet. The purpose of this paper is to investigate the factors affecting the acceptance and use of e-learning system. There are a number of implicit and explicit frameworks designed to inform e-learning practice. Some of them suggest key components that influence the quality of the e-learning experience: technology, pedagogy, organizational context and creativity. Instructor feedback and student learning styles, significantly affect the perceived learning outcomes of e-learning students. Namely, quality of education will significantly be enhanced if instructors modify their teaching styles to accommodate the learning styles of all students in their classes. When the teacher creates the lesson plan, it is desirable that he or she puts as many activities as possible which will reflect different learning styles. Whereas, students have diverse backgrounds, abilities, and knowledge bases, teachers who are able to use various instructional strategies have been shown to be more effective than those who just use single strategies.     

Supply chain simulator: A scenario-based educational tool to enhance student learning

Simulation-based educational products are excellent set of illustrative tools that proffer features like visualization of the dynamic behavior of a real system, etc. Such products have great efficacy in education and are known to be one of the first-rate student centered learning methodologies. These products allow students to practice skills such as critical thinking and decision-making. In this paper, a case is presented where a scenario-based e-learning product namely ‘supply chain simulator’ is developed at KFUPM for an introductory technology course. The product simulates a supply chain – a network of facilities and distribution systems that carries out the task of procurement and transformation of materials from manufacturer to customer. The product was put to test during four semesters and results of the survey conducted by the instructors and the students are presented. The results clearly suggest the benefits of using such a tool in enhancing student learning.     

Critical success factors for e-learning acceptance: Confirmatory factor models

E-learning, one of the tools emerged from information technology, has been integrated in many university programs. There are several factors that need to be considered while developing or implementing university curriculums that offer e-learning based courses. This paper is intended to specify e-learning critical success factors (CSFs) as perceived by university students. The published e-learning critical success factors were surveyed and grouped into 4 categories namely, instructor, student, information technology, and university support. Each category included several measures. The categorization was tested by surveying 538 university students. The results revealed 8 categories of e-learning CSFs, each included several critical e-learning acceptance and success measures. The level of criticality of each measure was represented by its validity coefficient. Confirmatory factor modeling approach was used to assess the criticality of the measures included in each CSF category.     

Comparing dropouts and persistence in e-learning courses

Several studies have been conducted related to dropouts from on-campus and distance education courses. However, no clear definition of dropout from academic courses was provided. Consequently, this study proposes a clear and precise definition of dropout from academic courses in the context of e-learning courses. Additionally, it is documented in literature that students attending e-learning courses dropout at substantially higher rates than their counterparts in on-campus courses. Little attention has been given to the key factors associated with such substantial difference. This study explores two main constructs: (1) academic locus of control; and, (2) students’ satisfaction with e-learning. Results show that students’ satisfaction with e-learning is a key indicator in students’ decision to dropout from e-learning courses. Moreover, dropout students (non-completers) reported to have significantly lower satisfaction with e-learning than students who successfully completed (completers or persistent students) the same e-learning courses. Additionally, results of this study show that the academic locus of control appears to have no impact on students’ decision to drop from e-learning courses.     

The influence of student characteristics on the use of adaptive e-learning material

Adaptive e-learning materials can help teachers to educate heterogeneous student groups. This study provides empirical data about the way academic students differ in their learning when using adaptive e-learning materials. Ninety-four students participated in the study. We determined characteristics in a heterogeneous student group by collecting demographic data and measuring motivation and prior knowledge. We also measured the learning paths students followed and learning strategies they used when working with adaptive e-learning material in a molecular biology course. We then combined these data to study if and how student characteristics relate to the learning paths and strategies they used. We observed that students did follow different learning paths. Gender did not have an effect, but (mainly Dutch) BSc students differed from (international) MSc students in the intrinsic motivation they had and the learning paths and strategies they followed when using the adaptive e-learning material.     

Learning motivation in e-learning facilitated computer programming courses

Computer programming skills constitute one of the core competencies that graduates from many disciplines, such as engineering and computer science, are expected to possess. Developing good programming skills typically requires students to do a lot of practice, which cannot sustain unless they are adequately motivated. This paper reports a preliminary study that investigates the key motivating factors affecting learning among university undergraduate students taking computer programming courses. These courses are supported by an e-learning system – Programming Assignment aSsessment System (PASS), which aims at providing an infrastructure and facilitation to students learning computer programming. A research model is adopted linking various motivating factors, self-efficacy, as well as the effect due to the e-learning system. Some factors are found to be notably more motivating, namely, ‘individual attitude and expectation’, ‘clear direction’, and ‘reward and recognition’. The results also suggest that a well facilitated e-learning setting can enhance learning motivation and self-efficacy.     

Software verification and graph similarity for automated evaluation of students’ assignments

ContextThe number of students enrolled in universities at standard and on-line programming courses is rapidly increasing. This calls for automated evaluation of students assignments.ObjectiveWe aim to develop methods and tools for objective and reliable automated grading that can also provide substantial and comprehensible feedback. Our approach targets introductory programming courses, which have a number of specific features and goals. The benefits are twofold: reducing the workload for teachers, and providing helpful feedback to students in the process of learning.MethodFor sophisticated automated evaluation of students’ programs, our grading framework combines results of three approaches (i) testing, (ii) software verification, and (iii) control flow graph similarity measurement. We present our tools for software verification and control flow graph similarity measurement, which are publicly available and open source. The tools are based on an intermediate code representation, so they could be applied to a number of programming languages.ResultsEmpirical evaluation of the proposed grading framework is performed on a corpus of programs written by university students in programming language C within an introductory programming course. Results of the evaluation show that the synergy of proposed approaches improves the quality and precision of automated grading and that automatically generated grades are highly correlated with instructor-assigned grades. Also, the results show that our approach can be trained to adapt to teacher’s grading style.ConclusionsIn this paper we integrate several techniques for evaluation of student’s assignments. The obtained results suggest that the presented tools can find real-world applications in automated grading.     

Multi-task MIML learning for pre-course student performance prediction

In higher education, the initial studying period of each course plays a crucial role for students, and seriously influences the subsequent learning activities. However, given the large size of a course’s students at universities, it has become impossible for teachers to keep track of the performance of individual students. In this circumstance, an academic early warning system is desirable, which automatically detects students with difficulties in learning (i.e., at-risk students) prior to a course starting. However, previous studies are not well suited to this purpose for two reasons: 1) they have mainly concentrated on e-learning platforms, e.g., massive open online courses (MOOCs), and relied on the data about students’ online activities, which is hardly accessed in traditional teaching scenarios; and 2) they have only made performance prediction when a course is in progress or even close to the end. In this paper, for traditional classroomteaching scenarios, we investigate the task of pre-course student performance prediction, which refers to detecting at-risk students for each course before its commencement. To better represent a student sample and utilize the correlations among courses, we cast the problem as a multi-instance multi-label (MIML) problem. Besides, given the problem of data scarcity, we propose a novel multi-task learning method, i.e., MIML-Circle, to predict the performance of students from different specialties in a unified framework. Extensive experiments are conducted on five real-world datasets, and the results demonstrate the superiority of our approach over the state-of-the-art methods.     

eTeacher: Providing personalized assistance to e-learning students

In this paper we present eTeacher, an intelligent agent that provides personalized assistance to e-learning students. eTeacher observes a student’s behavior while he/she is taking online courses and automatically builds the student’s profile. This profile comprises the student’s learning style and information about the student’s performance, such as exercises done, topics studied, exam results. In our approach, a student’s learning style is automatically detected from the student’s actions in an e-learning system using Bayesian networks. Then, eTeacher uses the information contained in the student profile to proactively assist the student by suggesting him/her personalized courses of action that will help him/her during the learning process. eTeacher has been evaluated when assisting System Engineering students and the results obtained thus far are promising.     

Quest for the Golden Rule: An effective social skills promotion and bullying prevention program

Everyday many students face bullying situations that they are ill equipped to manage. E-learning has recently emerged as a potentially effective tool in teaching children social skills, in addition to academic subject matter. Quest for the Golden Rule is one of the first bullying prevention e-learning programs available, designed by the Practi-Quest Corporation, for children in grades 2 – 5. The purpose of the current study was to explore data collected as part of standard program quality assurance practices to evaluate the impact of the gaming modules on how much children learned through interacting with the modules. Sample sizes ranged from 226 to 438 depending on the module; with approximately equal numbers of boys and girls. Following their interactions with each module, children’s knowledge of bullying and their identification of strategies to prevent bullying improved significantly. The majority of children reported that they enjoyed the game and felt confident that they could help solve bullying problems. Quest for the Golden Rule is an engaging, effective, and efficient means of raising awareness, fostering positive attitudes, and promoting effective problem-solving for bullying prevention in schools.     

Comparing grades in online and face-to-face writing courses: Interpersonal accountability and institutional commitment

In spite of benefits surrounding distance education programs, many online writing courses suffer from low student completion rates. Student retention has been identified as a concern in a number of studies of online education. We extend this discussion by examining the relationship of assessment of student work to retention, and comparing the grades students receive in online and face-to-face undergraduate writing courses. Our data point to what we call the “thrive or dive” phenomenon for student performance in online writing courses, which describes the disproportionately high percentage of students who fail or do not complete online courses compared to conventional, face-to-face courses. We extend this discussion on challenges related to student retention and propose instructional approaches for online learning that include the interpersonal accountability between teachers and students, as well as the institutional commitment necessary to ensure that students can succeed in online writing courses and programs.     

Evaluating the reliability and impact of a quality assurance system for E-learning courseware

Assuring e-learning quality is of interest worldwide. This paper introduces the methods of e-learning courseware quality assurance (a quality certification system) adopted by the eLQSC (e-Learning Quality Service Centre) in Taiwan. A sequential/explanatory design with a mixed methodology was used to gather research data and conduct data analyses. Sixty-seven e-learning courseware applications were evaluated using the e-Learning Courseware Quality Checklist (eLCQC). Analyses of item difficulty, item discrimination and generalizability indicated that the eLCQC provides high-quality evaluations. After participating in the certification process, the e-learning producers reported having more confidence in enhancing e-learning courseware quality. The certification experience also helped the producers to change their previous design concepts about e-learning courseware and to develop new concepts; further, their increased confidence about courseware design also enhanced their confidence in marketing courseware.     

Can structured representation enhance students' thinking skills for better understanding of E-learning content?

This paper proposes an e-learning model to assess the effects of online structured representation of content on learners' understanding. We designed a structured representation based on the theory of distributed cognition that provides seven segments for reading research articles. The study hypothesized that motivation, attention, and interactivity are essential factors that affect students' thinking skills for understanding e-learning content. To investigate and confirm the effect of these factors on the students' thinking skills, we designed a survey and analyzed the responses of 210 university students concerning the proposed structured representation. The results revealed that motivation, attention, and interactivity did contribute to the students' thinking skills. They also demonstrated that the structured representation helped students achieve an adequate level of thinking skills as they read research articles, which had a positive effect on their understanding. This finding demonstrates that structured representation has significant potential as a learning tool and that structure-based e-learning can influence students' metacognitive activities and facilitate their understanding.     

Assessing information quality of e-learning systems: a web mining approach

E-learning systems provide a promising solution as an information exchanging channel. Improved technologies could mean faster and easier access to information but do not necessarily ensure the quality of this information; for this reason it is essential to develop valid and reliable methods of quality measurement and carry out careful information quality evaluations. This paper proposes an assessment model for information quality in e-learning systems based on the quality framework we proposed previously: the proposed framework consists of 14 quality dimensions grouped in three quality factors: intrinsic, contextual representation and accessibility. We use the relative importance as a parameter in a linear equation for the measurement scheme. Formerly, we implemented a goal-question-metrics approach to develop a set of quality metrics for the identified quality attributes within the proposed framework. In this paper, the proposed metrics were computed to produce a numerical rating indicating the overall information quality published in a particular e-learning system. The data collection and evaluation processes were automated using a web data extraction technique and results on a case study are discussed. This assessment model could be useful to e-learning systems designers, providers and users as it provides a comprehensive indication of the quality of information in such systems.