Computer Based Education

In this meta-analysis, the aim was to determine the influence of using technology on academic achievement. Web of Science, ScienceDirect, Scopus, EBSCO and TR Index databases were searched for and a total of 2908 studies published between July 2005 and July 2019 were analysed. 122 experimental studies with control groups implemented in Turkey were included in this study. 144 effect sizes (ES) were calculated, covering a total of 10011 samples. When calculating the effect sizes, two different combinations, that is (1) mean, standard deviation, sample size and (2) sample size and p-value were used. Because the data were heterogeneous, the random-effects model was used to calculate ES. A mixed-effects model was preferred for the moderators coded in line with the research questions. As a result of meta-analysis using random-effects model, the overall effect of technology on academic achievement was large (ES = 0.89). In addition, as a result of the analysis using the mixed-effects model, the effect of technology on academic achievement was significantly different according to a technology-based learning environment, sample size and education level. There was no significant difference in the influence of technology on academic achievement in terms of field/course and technology-based learning environments based on learning theories. This study makes a significant contribution to the literature by determining the overall effect of technology on academic achievement and making a relatively comprehensive and new meta-analysis. Keywords: Educational technology; technology; computer-based instruction; academic achievement; meta-analysis. SUMMARY For more than half a century, research has focused on the influence of technology on learning outcomes. The development of technology causes changing the factors that affect academic success and the emergence of new teaching methods. With the development of technologies that can be used in learning environments, it is now compulsory to conduct research using different teaching methods. The increasing number of such experimental studies has necessitated new research methods. Meta-analysis is one of these research methods, which make inferences from the results of numerous experimental studies and thus provide clearer results to both practitioners and researchers. This study used a meta-analysis method, was performed in Turkey and examined experimental studies compared to conventional training and instructional methods using a variety of technologies. The overall aim was to make inferences about the effect on the success of technology. Thanks to this study, a more comprehensive and new meta-analysis is conducted to determine the overall impact of educational technologies on academic achievement. In this context, we looked for answers to the following questions: 1. What is the overall impact of technology on academic achievement? 2. How do technology-based learning environments affect academic achievement? 3. How does the foundation of learning theories in technology-based learning environments affect academic achievement? 4. Does the impact of technology on academic achievement differ according to education level of the sample? 5. Does the impact of technology on academic achievement differ according to disciplinary areas? 6. Does the impact of technology on academic achievement differ according to sample size? Web of Science, ScienceDirect, Scopus, EBSCO and TR Index database were scanned to examine Turkey-addressed articles published between 2005 and 2019 July and examined the effect of Technologies on academic achievement . The scope of this study consisted of 122 articles with control and experimental groups with statistically equivalent variables. All control group participants were educated only by the traditional method. The total number of samples covered by these articles was 10011 and total independent results were 144. Q statistics and I2 statistics which are developed as a complement of this statistics are examined. In this study, because the data were heterogeneous, random effects model was used to calculate the effect size (ES). Mixed effects model was preferred for the moderators coded in accordance with the research questions. The ESs were interpreted according to the rules developed by Cohen (1988) and Sawilowsky (2009). According to the findings, the smallest ES was 0.01 and the largest ES was 4.42. Thus, although there are studies showing that the impact of technology on academic achievement is very small, there are also studies showing a huge effect. Having calculated the ESs of the studies, 7 studies have a negative effect and 137 studies have a positive effect. This result indicates that technology does not always have a positive effect on and sometimes it may affect students' academic achievement negatively. The overall impact of technology on academic achievement was large (ESoverall= 0.89). This result shows that technology has a significant influence on academic achievement. The effects of technology-based learning environments used in the learning process on academic achievement differ significantly. This result indicates that not every technology-based learning environment will have the same positive impact on students' academic achievement. In this study, the effect of smart board, AR, mobile learning and digital games on academic achievement was found to be large. Blended learning and the use of caricature has been shown to be medium while video and virtual reality have a small effect on academic achievement. According to the results of this study, the use of technology for teaching purposes when based on the theories of learning has a large effect on academic achievement. However, the fact that any learning theory is not based on using technology has been found to have a large and almost identical effect. As a result, learning theories do not constitute a significant difference. According to the results of this study, the effect of technology differs according to education level. This result shows that the impact of technology at all educational levels is not the same. Therefore, technology affects academic achievement at a medium level in undergraduate education and at a large level at the high school, primary school, associate degree, and secondary school. On the other hand, the effect of technology on academic achievement does not differ significantly by course. In addition, technology has a medium and a higher impact on all courses except biology. We detected a very large ES in geography and history courses, a large ES in Turkish, social sciences, mathematics, science, and chemistry courses, and a medium ES in physics, textiles, computers, medicine, educational sciences, and English. In our study, the effect of technology on mathematics was large and the effect on medicine was medium. Another result was that the effect of technology on academic achievement does not differ significantly according to the field/course. In experimental studies, the effect of technology on academic achievement differs significantly according to the sample size. The highest ES was found in the sample range of 61-100. It was found that the ES was lower in the samples between 30-60 and 101 and above. As a result, one can claim that the use of technology in teaching environments increases academic achievement significantly. In this study, the overall effect of technology on academic achievement is determined by making inferences from experimental studies accross national literature. This should be taken into consideration when evaluating the results obtained in this meta-analysis, as the relevant experimental studies vary methodically and statistically, and there may be a risk of bias (Chen et al., 2018).

The aggregated assessment or hierarchical tree assessment Internet based software application is the result of a research and development (R&D) project in progress that is a part of a doctoral thesis in assessment. The developed software application is similar to already existing E-Learning and online assessment applications as MOODLE (http://www.moodle.org), WebCT and Blackboard (http://www.blackboard.com). The main discovery or improvement brought by this actual doctoral project is that the software application is able to assess knowledge, skills, abilities and objectives of team members that could have hierarchical relationships between them. The application is able to assess at the same time knowledge and skills of team members, team leaders and team managers that could supervise many team leaders. The application also has aggregation capabilities. The aggregation process in the software application allows the database to sort lists of students by criteria of age, highest scores or seniority to assign team members, team leaders or team managers appointments to some designated students.
This software application has been developed to replace traditional courses in presence. The application is designed to provide distance learning and assessment to persons who couldn’t attend classes. The application can easily be used by teachers and assessors to assess at distance courses, homework and performances. Its formative and summative assessment functionalities allows the application to present the course material contained in the curriculum and also to assess students while they perform complex assessment tasks included in the course schedule. The software application will allow schools and universities to do online course design and to qualify or graduate students that have used the application to follow distance courses.
This software application have three categories of users that are students, assessors and course administrators. The students, that are the first category of users, are learning the course material and perform the different assessment tasks of the curriculum as team members, team leaders and team managers. In the actual context of the software application, a team manager in training, equally defined as an administrator in training, is a student assigned to a management task allowing him to be the supervisor of team leaders. The team leader or the team administrator is not the course administrator or the system administrator. In the present application context, the course or the system administrator is a teacher or a computer system programmer in charge of the entry of course material in the system, the entry of student’s data and the grouping of the teams of students (aggregation function). The students are producing the course formative assessment of themselves (self assessment) and their teams while the course assessor is producing summative assessment of the students, the teams and the overall task. In the present application, the students does not provide marks or summative assessment. The formative assessment produced by the students consists of self-assessment, peers assessment, assessment by the team leader and the assessment of the team manager. The students can also give formative assessment on the level of realization of the task preformed by their team or the overall performance of the entire work done by all the course teams, this producing the entire formative assessment of all the work done by the course.
The second category of users are the assessors. The assessors are usually teachers, professors, course lecturers or instructors. The assessors have two main duties in the distance assessment process. They have to produce the summative assessment and to initiate the hierarchical aggregation process by grouping the students teams. The assessors are producing summative assessment by marking homework, exams, tasks and performances that students, teams and courses have produced during assessment tasks. The constitution of student’s teams is the hierarchical aggregation process of the software application. During this process, the assessor is gathering the teams of students and assign each student a position that could be team member, team leader or team manager. The team manager position is the highest assignment that a student could detain. The team manager or team administrator in training is the person who commands team leaders. Finally, the third category of users are the course administrators. As previously stated, the course administrators are the persons that are creating new distances courses in the application course database or modifying already existing courses. They can also manage students database by adding new students, modifying information on already registered students and removing students that have quitted courses.
Furthermore, the user interface allows the application to be suitable for mobile learning with wireless technologies portable computers as IPOD and Blackberry. Military personnel in combat missions will be able to learn new skills or courses and to qualify themselves even if they are away from the country and homeland military schools. Universities and schools will be able to extend their reach to students living far away from the campus or unable to assist courses in presence for any reasons. Traveling salesmen could learn information on new products, supervise sales teams and provide new products information or demonstrations directly at the client’s location.
To be able to use the software application as student, assessor or course administrator, the application provides this actual downloadable user’s guide. The guide introduces the design of a simple map using training course with only one task of navigation with map to be performed in teams. Despite the triviality of the described example, it will be easy for the reader to implement any type of courses with all kinds of complexity into the application after the study of the practical and theoretical concepts presented in the guide. The actual guide have over one hundred pages and this could be considered a large amount of reading for some users. However, the authors think that all the information contained in the guide is essential to use all the application functionalities and to implement the map using training course used as an example. All of the course material of the example is downloadable from the application for the users that wants to learn by doing their own implementation of the example.

In our day when science and technology are developing very rapidly, occupational groups have been affected by these rapid developments and have sought to exploit the benefits of these developments. The teaching profession, one of the earliest professions in human history, has not become insensitive to these rapid developments and has become a profession that requires a set of requirements and expertise. Teachers have been constrained to fulfill these requirements that technological developments put into practice while practicing their profession. and it is considered that the fulfillment of this obligation can be related to the belief in self-efficacy towards the profession. In this context; It is aimed to examine the relations between self-efficacy perceptions of prospective teachers and their attitudes towards computer-aided teaching.
Within the scope of this purpose, the following questions were sought:
1.Does the vocational qualification of the prospective teachers differ significantly from the facultyof graduation they graduate?
2.Does the profession's self-efficacy of the prospective teachers differ significantly according tothe division they have studied?
3.Does the teacher's attitudes towards CAE differ significantly from the faculty he / she isstudying?
4.Does the teacher's attitudes towards CAE differ significantly in relation to the division they havestudied?
5.Are the attitudes towards teacher self-efficacy sub-dimensions to CAE a significant predictor?