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Scholarly Research Journal for Interdisciplinary Studies, Online ISSN 2278-8808, SJIF 2016 = 6.17, www.srjis.com UGC Approved Sr. No.49366, JAN-FEB 2018, VOL- 5/43

EFFECT OF WEB BASED INSTRUCTIONAL STRATEGY ON ACHIEVEMENT IN COMPUTER SCIENCE IN RELATION TO SELF-REGULATED LEARNING Ram Mehar1, Ph.D. & Avneet Kaur2, Ph.D. 1

Assistant Professor, Department of Education, USOL, Panjab University, Chandigarh,

Punjab, India 2

Assistant Professor, Khalsa College of Education, Ranjit Avenue, Amritsar

The present study investigates the effect of web based instruction on achievement in Computer Science in relation to self-regulated learning. The data was collected from 300 XI class students of four private schools of Amritsar City affiliated to Central Board of Secondary Education, New Delhi. The Achievement Test in Computer Science consisting of 60 items and self-regulated learning Scale was developed and standardized by investigator to categorise students into high, average and low self-regulated learning students were used to collect the data. Experimental group was taught using web based instructional strategy and the control group was taught by conventional mode of instruction. Statistical techniques such as Mean and S.D were used in the analysis of data. F-ratio and t-test were employed to find significance of difference between means related to different groups and variables. The data was analysed using Analysis of Variance (2Ă—3) and following conclusions were drawn: (i) The achievement of group taught through web based instructional strategy was significantly higher than that of conventional method of teaching. (ii) The performance of high selfregulated learning group was significantly higher than that of average and low self-regulated learning group on achievement in computer science. (iii)There was no significant interaction effect of instructional strategies and self-regulated learning on achievement in computer science. Keywords: Web-Based Instructions, Achievement in Computer Science, Self-Regulated Learning.

Scholarly Research Journal's is licensed Based on a work at www.srjis.com

Introduction The advancements in technology have created a possibility for new ways of teaching and learning. The contributions of web in the teaching-learning process are the best among other computer innovations. The great potential of web can be fully utilized and taken advantage of, if teachers think about teaching and learning in new ways and master the use of new technology skills along with their students. The students of 21 st century need to be fully engaged which requires the use of new constructive methods of teaching and learning with the help of technology tools and resources and providing them appropriate learning environment. The technology based instructional programmes help students continue learning Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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outside the classroom by providing study questions, activities, and even tests and quizzes for a class. The use of World Wide Web (www) as an instructional tool is gaining importance as many teachers and trainers have started incorporating it into their daily instructions. In mid of 1990s, the use of web for educational purposes was explored. Today, the web is on the way of being an important learning environment which provides students with a new and rich style of learning. The introduction of technology into the classroom is changing the nature of imparting education to students by allowing them to explore new areas of learning and thinking by using their all sense organs. It has changed the iconic tools of the trade. Students discover knowledge through inquiry and experimentation rather than by merely memorizing facts in a teacher dominated classroom setting where the teacher uses words and at the most charts and models. Overlays and handouts are stationary visuals, while technology based materials are interactive and fully animated that offer teachers an opportunity to move away from instructional strategies that focus on presentation of abstract information to the passive learner to an active process in which meaning is developed on the basis of experience (Kaur, 2012). In the constructivist view, the learner is building an internal representation of knowledge and a personal interpretation of experience (Ranade, 2001). Education delivered with the help of internet is referred to by many names such as web based instruction, web based learning, web-based teaching, e-learning and onlinelearning. Web based instruction is the application of a repertoire of cognitively oriented instructional strategies within a constructivist and collaborative learning environment, utilizing the attributes and resources of the World Wide Web (Relan & Gillani, 1997). Khan (1997) defines web based instruction as a hypermedia-based instructional program which utilizes the attributes and resources of the World Wide Web to create a meaningful learning environment where learning is fostered and supported. Just as traditional instruction (whether teacher-centered or student-centered) is executed within the structural patterns which are in place, web-based instruction is situated within different contextual assumptions. First, it is assumed that the learner has access to the world wide web at all times, and is allowed to explore it in a self-determined or guided sequence. Second, web-based instruction would function best in a constructivist environment, indeed; the use of the medium in any other way would defeat its purpose in the instructional process. Third, the teacher "dethrones" him/herself as the disseminator of Information, and becomes a facilitator for finding, assessing, and making meanings from the information discovered from a variety of media. Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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Fourth, learning occurs in an interdisciplinary fashion without regard to the attainment of learning objectives within a fixed time (Relan & Gillani, 1997). Web-based instruction is facilitated by network-based technologies that afford collaborative learning experiences and can provide learners with flexible access to materials at various times and/or locations. Although web-based instruction is primarily associated with a traditional view of distance education depicting only situations when learners are geographically dispersed, this form of instructional delivery may also be effectively used as a supplement to traditional, face-to-face, classroom, based activities. Specific features of webbased instruction provide multiple opportunities for student-to-content, student-to-student, and student-to-instructor interaction (Moore & Kearsley, 1996). Web-based instruction offers multiple dimensions of use in education and training environments. As with computer-based instruction, it is capable of providing direct instruction to meet individual learning objectives. Due to its networking capability, the web can play additional roles. These include promoting and facilitating enrolment into courses, availing the syllabus or program of instruction, posting and submitting assignments, interacting with instructors and fellow students, collaboration on assignments, and building learning communities. The web has become a powerful tool for learning and teaching at a distance. Its inherent flexibility allows application in a variety of ways within an educational context, ranging from simple course administration and student management to teaching entire courses online. Each of these types of use works towards a different goal. These goals should be recognized when evaluating the use of the web. For example, an instructor may hold face-to-face lectures in a classroom but post the class syllabus, assignments, and grades on the web. In this case, it may not be appropriate to evaluate the use of the web with respect to learning outcomes since the web was not used in a direct instructional role (Olson & Wisher, 2002). Achievement contributes to the educational growth and development. It is measured and assessed by achievement test and compared to the set norms to evaluate an individualâ€™s performance. Achievement refers to the amount of knowledge gained by the student in different subjects of study. It motivates the students to work hard and learn more. It also helps the teachers to diagnose and provide remedy to studentâ€™s weaknesses after evaluation and helps the teacher to know whether their teaching methods are effective or not and bring improvements accordingly. In academic context, achievement concerns the development of Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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knowledge, understanding and acquisition of skills. Therefore, achievement may be defined as a measure of cognitive learning outcomes of Bloom’s taxonomy at knowledge, understanding and higher order levels in a specific subject or a group of subjects. Achievement is an end product of learning and its level and performance are affected by many factors. Learners’ achievement at a particular point of time is a function of cumulative input of the variables which interact with the innate abilities or “The Learning potential of the students’’ (Hanushek, 1979). These interactions create an environment that affects the achievements of student. A plethora of research studies are available which have concluded that superior intellectual endowment is considered as an input variable that plays decisive role in setting up limits to achievement. Garg and Chaturvedi (1992), Noor-Jahan (1994) and Vaghela (2000) have shown significant relationship between intelligence and achievement. Achievement in computer science is influenced by various factors such as technology savviness, interest in hardware and software of a computer, practical usage of a computer, self regulatory practices on the part of student etc. The present study was limited to the instructional strategy and self regulation as two variables or factors that tend to influence achievement in computer science. Self-regulated learning is a process that assists students in managing their thoughts, behaviours and emotions in order to successfully navigate their learning experiences. This process occurs when a student’s purposeful actions and processes are directed towards the acquisition of information or skills (Zumbrunn, Tadlock & Roberts, 2011). Generally, models of self-regulated learning are separated into phases. A popular cyclical model discusses three distinct phases: Forethought and planning, performance monitoring, and reflections on performance (Pintrich & Zusho, 2002; Zimmerman, 2000). During the forethought and planning phase, students analyze the learning task and set specific goals toward completing that task. When students learn unfamiliar topics, however, they may not know the best ways to approach the task or what goals might be the most appropriate. Teachers and/or more experienced peers often can instruct students on effective approaches in cases like these. Next, in the performance monitoring phase, students employ strategies to make progress on the learning task and monitor the effectiveness of those strategies as well as their motivation for continuing progress toward the goals of the task. Unfortunately, when strategies are new, students sometimes revert to using more familiar and perhaps ineffective strategies. For example, students may lapse into using the familiar Copyright © 2017, Scholarly Research Journal for Interdisciplinary Studies

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strategy of flash cards to study new vocabulary words because it might seem easier than the new, effective strategy presented by the teacher. Whereas taking the time necessary to practice and learn the new strategy could lead to meaningful learning, studentsâ€™ use of their fall-back strategy will likely leave them with a considerably less effective means to their learning. Close teacher monitoring and specific feedback can help students learn to use new strategies with fluency, especially if students face frustration. In the final reflection on performance phase, students evaluate their performance on the learning task with respect to the effectiveness of the strategies that they chose. During this stage, students also must manage their emotions about the outcomes of the learning experience. These self-reflections then influence students future planning and goals, initiating the cycle to begin again (Zumbrunn, Tadlock & Roberts, 2011). In the last ten years, several educational psychologists (Dabbagh & Kitsantas, 2004; Hartley & Bendixen, 2001; Schunk & Zimmerman, 1998) have suggested that students require considerable motivation and self-regulation to stay engaged, guide their cognition, and regulate their effort in online situations. The highly independent nature of online learning is thought to be due, in part, to the lack of structure and guidance that normally comes from face-to-face, social interactions with an instructor and other students (Moore & Kearsley, 2005). Need and Significance India is a country of large and diverse population. People belong to varied social differences like cultures, socio-economic status, live at varied geographical locations and many more. When we think of educating such a diverse population and achieving the best results in education, we cannot simply rely upon old and traditional methods of teaching and learning. Also, in India we have many such students who are unable to attend schools regularly on health grounds or due to certain family or financial problems. Web based instructional strategy can be used either for students who prefer online courses, distance education or for assisting classroom instruction. This strategy requires ability of using computers and assessing internet resources. The rising popularity and importance of webbased instruction in the field of education requires checking the effectiveness of web-based instruction by comparing it to other classroom instructional techniques and find out what factors influence the use of it for making the teaching and learning process effective. In the present study the effectiveness of web based instructional strategy is studied on achievement Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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in computer science relation to self-regulated learning, as the investigator felt that there is a need that web based instruction as an instructional strategy should be incorporated in schools because it has the potential to develop the various skills and abilities among students and make them better informed and high achievers. Objectives The study was conducted to achieve the following objectives: 1.

To compare the achievement of groups of students taught through web based instructional strategy and conventional teaching strategy in computer science.

To compare the achievement of high, average and low groups of students on selfregulated learning.

To examine the interaction effects between instructional strategies and self-regulated learning on achievement in computer science.

Hypotheses The study was designed to test the following hypotheses: 1.

There will be no significant difference in the achievement of group taught through web based instructional strategy and conventional teaching strategy in computer science.

There will be no significant difference in the achievement of group having high selfregulated learning than that of average and low high self-regulated group of students in computer science.

There will be no significant interaction effect of instructional strategies and high selfregulated on achievement in computer science.

Sample In the present study, purposive sampling was initially employed to select those schools which have LAN facility and had opted for computer science subject in XI class and then random sampling technique was used to select five schools from amongst them. The sample in the present study was drawn at the school and student level. After selecting the schools, the intact sections of each school were randomly taken for experimental and control groups from the four schools. The present study was conducted on initial sample of 300 students of class XI who had opted for computer science subject from English medium schools of Amritsar, affiliated to Central Board of Secondary Education, New Delhi. The school-wise distribution of the sample is as shown the table-1. Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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Table-1: The school wise distribution of the sample S. Name of the Schools No. 1 Shri Guru Harkrishan International School, Amritsar

Experimental Group 25

Control Group 25

Total

2 3 4

25 50 50 150

50 100 100 300

The Millenium School, Amritsar Khalsa Public School, G.T. Road, Amritsar The Senior Study II, Amritsar Total

Design For the purpose of present investigation a pre and post-test factorial design was employed. In order to analyze the data a analysis of variance (2Ă—3) was used for the two independent variables viz. instructional strategy and self-regulated learning. The impact of instructional strategy was examined at two levels, namely web based instructional strategy and conventional teaching strategy. The classification of self-regulated group was done at three levels viz. high, average and low self-regulated learning. The main dependent variable was the achievement in computer science, which was calculated as the difference in post- test and pre-test scores for computer science subject. Tools Used The following tools were used for data collection: 1

General Mental Ability Test by Jalota (1972) was used to access the intelligence of the students for matching the group.

Self-Regulated Learning Scale developed by the investigators.

An Achievement Test in Computer Science developed by the investigators.

Instructional Material for Web Based Instructional Strategy and Conventional Teaching Strategy on five topics such as introduction to database concepts, data types, keys and their types, classification of SQL commands and creating and using a database using MYSQL of XIth class computer science subject were developed by investigators.

Procedure After the selection of the sample and allocation of students to the two instructional strategies, the experiment was conducted in six phases. Firstly, the investigator made necessary arrangements with the Principal of the school selected for the experiment. Secondly, General Mental Ability Test to assess intelligence was administered for matching of the students. Thirdly, the self-regulated learning scale was administered in each school in Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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order to identify the self-regulated learning levels of the students. Fourthly, an achievement test in computer science as pre-test was administered to the students of experimental and control groups. The answer-sheets were scored to obtain information regarding the previous knowledge of the students. Fifthly, the experimental group was taught through web based instructional strategy and control group was taught through conventional teaching strategy by the investigators. Sixthly, after the completion of the course, the achievement test in computer science as post-test was administered to the students of both the groups. The answer-sheets were scored with the help of scoring key. ANALYSIS AND INTERPRETATION OF THE RESULTS 1.

Analysis of Descriptive Statistics The data were analyzed to determine the nature of the distribution of scores by

employing mean and standard deviation. The Analysis of Variance (2Ă—3) was used to test the hypotheses related to web based instructional strategy, conventional teaching strategy and self-regulated learning levels. The mean and standard deviation of different sub groups have been presented in table- 2, 3, 4, 5 & 6. The obtained mean gain scores on achievement in computer science for experimental and control group has been given in table-2. Table-2: A summary of descriptive statistics of mean gain achievement scores of experimental and control group Variables

Experimental Group N Mean SD

High Self-Regulated Learning 40 Average Self-Regulated 70 Learning 40 Low Self-Regulated Learning 150 Total Self-Regulated Learning

25.43 20.87 13.05 20.00

6.86 6.75 4.96 7.82

Control Group N Mean SD 40 17.87 5.45 70 14.76 5.54 40 15.05 6.55 150 15.67 5.92

Total Mean

80 140 80 300

21.65 17.82 14.05 17.84

6.16 6.15 5.76 6.88

Source: Field Study, 2016 To substantiate the data presented in table-2, a bar diagram was drawn to depict mean gain achievement scores for high, average, low and total self-regulated learning groups of experimental and control group has been given in fig-1.

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Mean Gain Achievement Scores 25.43

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High Self-Regulated Learning Average Self-Regulated Learning Low Self-Regulated Learning

Total Self-Regulated Learning 21.65

20.87 20

Frequency

17.87 14.76 15.05

17.83

17.81 15.67

14.05

13.05

0 Experimental Group

Control Group

Total

Scores Fig-1: Bar diagram showing comparison of mean gain achievement scores of experimental and control group. The table-2 and fig-1 shows that the means gain achievement score of total selfregulated learning of experimental group is 20 and of control group is 15.67. This shows that the mean gain achievement score in computer science is high for the experimental group than that of control group with respect to self-regulated learning. Further the above table reveals that the mean gain achievement score of high self-regulated learning group of experimental group is 25.43 and of control group is 17.87. This shows that the mean gain achievement scores of high self-regulated learning is high for the experimental group than that of control group. And for average self-regulated learning, it is observed that mean gain of experimental group is 20.87 and of control group is 14.76. This shows that the mean gain achievement scores of average self-regulated learning is high for the experimental group than the control group. Further for low self-regulated learning, it is observed that mean gain of experimental group is 13.05 and of control group is 15.05. This shows that the mean gain achievement score in computer science of low self-regulated learning group is low for experimental group than that of the control group. Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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Analysis of Variance on Gain Achievement Scores The mean of different sub-groups, sum of squares, degree of freedom, mean sum of squares and the F - ratio have been presented in table-3. Table-3: Summary of Analysis of Variance (2×3) factorial design Source of Variation

Sum of Squares

Mean of Sum of Squares

Instructional Strategy (A) Self-Regulated Learning (B) Interaction (A× B) Error Terms

958.99 309.41 29.95

1 2 2

958.99 154.70 14.97

9901.11

294

33.68

F-ratio 28.47** 4.59* 0.45

**Significant at 0.01 level (Critical Value 3.87 at 0.05 and 6.72 at 0.01 level, df 1/294) (Critical Value 3.03 at 0.05 and 4.68 at 0.01 level, df 2/294) Web Based Instructional Strategy (A) It is observed from the table-3 that the F-ratio for difference in gain achievement scores of web based instructional strategy and conventional teaching strategy is 28.47, which in comparison to the table value was found significant at 0.01 levels of significance. It shows that the groups are different beyond the contribution of chance. Hence, the hypothesis H1: There will be no significant difference in the achievement of group taught through web based instructional strategy and conventional teaching strategy in computer science, is rejected. The result indicates that achievement of group taught through web based instructional strategy is much higher than that of conventional teaching strategy. In order to probe deeper, F-ratio is followed by t-test. The values of the t-ratio for different combinations have been given in the table-4. Table-4: t-ratios for mean gain achievement scores of experimental and control group Variable Gain Achievement Scores

Experimental Group N Mean SD 150 20.00 7.82

Control Group N Mean SD 150 15.67 5.92

SED

t-value

0.80

5.41**

**Significant at 0.01 level (Critical Value 1.97 at 0.05 and 2.59 at 0.01 level, df =298) A bar diagram has drawn to depict the mean gain achievement scores in computer science has been presented in fig- 2.

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Mean Gain Achievement Scores 25

20.00 Frequency

15.67 15 10 5 0

Experimental Group

Control Group Scores

Fig-2: Bar diagram showing comparison of mean gain achievement scores of experimental and control group It is observed from the table-4 and fig-2 that the mean gain achievement scores of experimental group taught through web based instructional strategy is 20.00, which is higher than the corresponding mean gain scores of 15.67 for the control group taught through conventional teaching strategy. The t-value testing the significance of mean gain difference on achievement in computer science of experimental and control group is 5.41, which in comparison to the table value was found significant at 0.01 levels of significance. Hence, the hypothesis H1of significant difference is accepted in case of web based instructional strategy and conventional teaching strategy irrespective of grouping across other variables. The result indicates that the students taught through web based instructional strategy perform significantly better than that of students taught through conventional teaching strategy. Self-Regulated Learning (B) It has been seen from the table-3 that the F-ratio for difference of mean gain achievement scores of the different self-regulated learning groups is 4.59, which in comparison to the table value was found significant at 0.05 levels of significance. Hence, the hypothesis H2: The achievement of high self-regulated learning group is significantly higher than that of average and low self-regulated learning group of students in computer science, is accepted. The result indicates that the high, average and low self-regulated learning groups do not yield equal level of achievement. Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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To investigate further F-ratio is followed by t-test. The values of the t-ratio for different combination have been given in table- 5. Table-5: t-ratio for different combinations of different self-regulated learning groups High SelfRegulated Learning N Mean SD 82 19.40 7.10

Variables

High Self-Regulated Learning N Mean SD 82 19.40 7.10 Average Self-Regulated Learning N Mean SD 136 18.04 7.40 Low Self-Regulated Learning N Mean SD 82 15.91 6.80

Average SelfRegulated Learning Low Self-Regulated N Mean Learning SD N Mean SD 136 18.04 82 15.91 6.80 7.40

1.35

3.20**

2.17*

* Significant at 0.05 level

** Significant at 0.01 level

A bar diagram has been drawn to depict the mean gain scores of high, average and low self-regulated learning groups on achievement in computer science and presented in Fig.-3. Mean Gain Achievement Scores 25 19.40

18.04

Frequency

15.91

15 10 5 0 High Self-Regulated Learning

Average Self-Regulated Learning

Low Self-Regulated Learning

Scores

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Fig-3: Bar diagram showing comparison of mean gain achievement scores of different levels of self-regulated learning It is observed from the table-5 and fig-3 that the mean gain achievement scores of high self-regulated learning group is 19.40, which is higher than the corresponding mean gain achievement score of 18.04 for average self-regulated learning group. The t-ratio for difference in gain scores of high and average self-regulated learning is 1.35, which in comparison to the table value (t

0.05=1.97,

df 216) was not found significant even at 0.05 level

of significance. Hence, the hypothesis of significant difference is rejected in case of high and average self-regulated learning irrespective of grouping across other variables. The result indicates that the high and average self-regulated learning groups of students do not differ significantly in respect of gain scores on achievement. It is evident from the table-5 and fig-3 that the mean gain achievement scores of high self-regulated learning group is 19.40, which is higher than the mean gain achievement score of 15.91 for low self-regulated learning group. The t-ratio for difference in gain scores of high and low self-regulated learning group is 3.20, which in comparison to the table value (t 0.01=2.61,

df 162) was found significant at 0.01 level of significance. Hence, the hypothesis of

significant difference is accepted in case of high and low self-regulated learning irrespective of grouping across other variables. The result indicates that high self-regulated learning group of students perform significantly better than that of low self-regulated learning group of students. It is clear from the table-5 and fig-3 that the means gain achievement score of average self-regulated learning group is 18.04, which is higher than the corresponding mean gain achievement score of 15.91 for low self-regulated learning group. The t-ratio for difference in gain scores of average and low self-regulated learning group is 2.17, which in comparison to the table value (t 0.05=1.97, df 216) was found significant at 0.05 level of significance. Hence, the hypothesis of significant difference is accepted in case of average and low self-regulated learning irrespective of grouping across other variables. The result indicates that average selfregulated learning group of students perform significantly better than that of low selfregulated learning group of students. Interaction between Instructional Strategies and Self-Regulated Learning (A Ă— B) It is evident from the table-3 that the F-ratio for interaction between web based instructional strategy and self-regulated learning is 0.45, which in comparison to table value Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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was not found significant even at 0.05 levels of significance. It indicates that variable of instructional strategy does not interact with self-regulated learning to yield significant difference in respect of gain achievement scores. Hence, the hypothesis H3: There exists significant interaction effect of instructional strategies and self-regulated learning, is rejected. The result indicates that there is no a significant difference in the gain achievement scores in computer science due to interaction effect of instructional strategies and self-regulated learning. Discussion of the Results The finding of present study reveals that web based instructional strategy was more effective than that of conventional teaching strategy on achievement in computer science. Hence, the hypothesis H1: There will be no significant difference in the achievement of group taught through web based instructional strategy and conventional teaching strategy in computer science, is rejected. The results are supported by the findings of Schutte (1997), who found that students taught through virtual class on World Wide Web scored an average of 20% higher than the traditional class on both examinations. Thiele, Allen and Stucky (1999) found that the web based format increased collaborative learning and improved computer skills. Owston and Wideman (1999) found that grades were significantly higher for the web-based classes than for the face-to-face classes, which were significantly higher than for the correspondence classes. Daniel and Rohaida (2001) indicated that the students had a better understanding of the topics taught using web based instructions. Suwanbenjakul (2002) found that English learning achievement of students in the experimental group was higher than those of students in the control group. Kearns, Shoaf and Summey (2004) found that students in the web-based course scored significantly higher than those taught using traditional methods. Uzunboylu (2004) found that the English language grammar achievement of the experimental groupâ€™s subjects was higher than the control groupâ€™s subjects. Noisri (2005) found that the learning achievement of students with the web based instruction course was significantly higher than that of the students with normal teaching. Sengel (2005) found that there were significant differences between the pre-tests and post-tests of the science achievement test and attitude scale. Apichatibutarapong, Worrachittanon, Tenissara, Vongsirojgul and Petsuwan (2008) found that web-based instruction was effective in teaching Information Technology, Mathematics and Statistics, and Sciences. Mugan (2008) found that web intensive instruction Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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improved scores from pre-test to post-test on achievement in Biology. Kaur (2012) found that students achieved higher when taught through web based instruction as compared to conventional mode of instruction. Sudha and Amutha (2015) found that higher secondary learners of control group and experimental group differ significantly in the pre-test and posttest on achievement in Chemistry. The results are contradicted by the findings of Davies and Mendenhall (1998), Rattanavijai and Sharma (2003) who reported no difference in the achievement of students taught through web based instruction and traditional method of instruction. Kearns, Shoaf and Summey (2004) found that the satisfaction level of students in traditional classrooms was still more than those of web based course. Khatony, Nayery, Ahmadi, Haghani and VehvilainenJulkunen (2009) found that there was no significant difference between the groups in either the pre-test or the post-test scores in the knowledge test. Kuo (2016) reported that synchronous web-based instruction affects thinking styles and creativity of teachers and students. The finding of the present study indicates the performance of high self-regulated learning group was better in computer science than that of average and low self-regulated learning groups. The average self-regulated learning group performed better in computer science than the low self-regulated learning group. Hence, the hypothesis H2:The achievement of high self-regulated learning group is significantly higher than that of average and low self-regulated learning group of students in computer science, is accepted. The findings are supported by Pintrich and De-Groot (1990) who found that depending on the outcome measure, self-regulation, self-efficacy, and test anxiety emerged as the best predictors of performance. Staudt (1995) found that self-regulated learning strategies were positively correlated with achievement in English and Chemistry. Eom (1999) found that meta-cognitive and motivational strategies significantly influenced the overall model to predict achievement. Cobb (2003) found that time and study environment management and intrinsic goal orientation categories reported significant findings in their relationship to academic performance. Gagnon (2011) found that self-regulation capacity appears to be an important skill that may help both physicians and medical students to meet the demands of the medical profession and maintain an adequate level of well-being and performance in their work and daily life.

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The results are contradicted by the findings of Lindsey-Randall (2008) who found that various motivational variables have no significant relationship with their achievement or persistence. McManus (2000) found that highly self-regulating learners learn poorly in mostly linear web-based hypermedia learning environments, where they have very few choices, while medium self-regulating learners learn poorly in highly nonlinear environments where they are given too many choices. The finding of the present study further indicates that the interaction effect among instructional strategies and self-regulated learning did not yield significant difference in mean gain scores on achievement in computer science. Hence, the hypothesis H3: There exists significant interaction effect of instructional strategies and self-regulated learning on achievement in computer science, is rejected. The results are contradicted by the findings of Hargis (2000) who found that the ability to self-regulate one's learning appears to be an ideal characteristic for learning on-line via the internet. Yukselturk and Bulut (2005) in their study indicated that the effect of self-regulation variable on studentsâ€™ programming achievement was statistically significant. Bell (2006) through his study revealed that self-regulated learning was a significant predictor in the model of learning achievement in asynchronous online courses. Findings 1.

The achievement of group taught through web based instructional strategy was found significantly higher than that of conventional method of teaching.

The performance of high self-regulated learning group was significantly higher than that of average and low self-regulated learning group on achievement in computer science. The further analysis revealed that: (i)

The mean gain score of high self-regulated learning groups was not significantly higher than that of average self-regulated learning group.

(ii)

The mean gain score of high self-regulated learning group was significantly higher than that of low self-regulated learning group.

(iii)

The mean gain score of average self-regulated learning group was significantly higher than that of low self-regulated learning group.

There was no significant interaction effect of instructional strategies and self- regulated learning on achievement in computer science.

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Conclusion The present study reveals that web based instructional strategy gives better results than conventional teaching strategy for secondary school students. This could be because the students are able to access the web-based course from anywhere and at any time and at their own pace. Further, the self-regulated learning also slightly affects the achievement of computer science students in web based teaching-learning environment. The instructional strategies and self-regulated learning did not produce significant interactional effect on achievement scores. The study recommends the use of web based instructional strategy for better achievement of secondary school students in computer science. The self-regulated learning though did not seem to be a good predictor of achievement of students in computer science in web based learning environments. Hence, appropriate training to use computer and internet and self regulate must be given to achieve good results using web based instructional strategy. References Apichatibutarapong, S., Worrachittanon, W., Tenissara, R., Vongsirojgul, N., &Petsuwan, S. (2008, March). Effects of web-based instruction on Thai students' achievement: A meta-analysis. In Proceedings of the seventh International Association of Science and Technology for Development (IASTED) International Conference on Web-Based Education(pp. 105107). Anaheim, CA, USA: ACTA Press. Retrieved May 5, 2014 fromhttp://www.actapress.com/Abstract.aspx?paperId=32839 Bell, P. D. (2006). Can factors related to self-regulated learning and epistemological beliefs predict learning achievement in undergraduate asynchronous web-based courses? Perspectives in Health Information Management, 3(7), 1-17. Cobb, R. (2003). The relationship between self-regulated learning and academic performance in webbased courses. Unpublished Ph.D. Thesis, Blacksburg, Virginia: Virginia Polytechnic Institute and State University. Retrieved July 26, 2014 from http://scholar.lib.vt.edu/theses/available/etd03212003130332/unrestricted/srlonline_dissertation.pdf Dabbagh, N., & Kitsantas, A. (2004). Supporting self-regulation in student-centered web-based learning environments. International Journal on E-Learning, 3(1), 40-47. Daniel, E. S. G., & Rohaida, M. S. (2001). Elemental education.The Science Teacher, 68(9), 50–53. Davies, R., & Mendenhall, R. (1998).Evaluation comparison of online and classroom instruction for HEPE 129—Fitness and lifestyle management course. Salt Lake City, UT: Brigham Young University. Eom, W. (1999). The effects of self-regulated learning strategy on academic achievement in a computer-networked hypertext/hypermedia learning environment. Unpublished Ph.D. Dissertation, Florida, United States: The Florida State University. Retrieved August 9, 2014 from http://search.proquest.com/docview/ 304510504?accountid=28367. Gagnon, M. (2011). Exploring the self-regulation of physicians and medical students in relation to their well-being and performance.Unpublished Ph.D. Dissertation, Ottawa, Canada: University of Ottawa. Retrieved July 14, 2014 from Copyright © 2017, Scholarly Research Journal for Interdisciplinary Studies

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