نوع المستند : المقالة الأصلية
المؤلف
Ministry of education KUWAIT
المستخلص
الكلمات الرئيسية
الموضوعات الرئيسية
کلية التربية
کلية معتمدة من الهيئة القومية لضمان جودة التعليم
إدارة: البحوث والنشر العلمي ( المجلة العلمية)
=======
KNOWLEDGE OF AND ATTITUDES TOWARDS USING THE INTERNET FOR LEARNING: GENDER AND DISCIPLINE
By
BADER ALFELAIJ
Ministry of education
KUWAIT
} المجلد السابع والثلاثون– العدد الخامس – مايو 2021م {
http://www.aun.edu.eg/faculty_education/arabic
الملخص:
في الکويت ، يجب على المعلمين والمعلمين تشجيع الطلاب على استخدام الإنترنت بشکل أکبر لمصلحتهم. ومع ذلک ، أعتقد أنه من المهم أولاً استکشاف معرفة الطلاب ومواقفهم تجاه قبول الإنترنت واستخدامه ، قبل مطالبتهم باستخدامها. والأهم من ذلک ، نحتاج إلى تحديد مدى تأثير الاختلافات بين الجنسين والانضباط على قبول واستخدام الإنترنت. لذلک ، تهدف هذه الدراسة إلى استکشاف المعرفة والمواقف تجاه أهمية الإنترنت ، عند السعي إلى تعزيز تعلم الطلاب والطالبات في (PAAET) في الکويت. علاوة على ذلک ، سيتم أيضًا استکشاف تأثير الفروق بين الجنسين والانضباط على هذا القبول والاستخدام في PAAET. کانت العينة المختارة عبارة عن عينة ملائمة ، تتکون من 30 معلمًا (15 ذکرًا و 15 أنثى) قبل الخدمة من خلفيات مختلفة (أي من حيث الجنس والانضباط). تم استخدام استبيان لجمع البيانات. تکشف النتائج أنه لا توجد فروق متعلقة بجنس الطلاب على مستويات المعرفة. ومع ذلک ، کانت هناک اختلافات تتعلق بالانضباط. علاوة على ذلک ، أظهرت النتائج وجود فروق بين الطلاب والطالبات فيما يتعلق باتجاهاتهم لاستخدام الإنترنت ، مع اختلاف لصالح الطلاب الذکور. على الرغم من أنه من المسلم به أن نطاق الدراسة وقابليتها للتعميم محدودان ، نظرًا لعينتها الصغيرة وغير التمثيلية ، يمکن اعتبار هذه الدراسة بمثابة تقدم أولي مناسب في منطقة الدراسة ، والتي قد تکون مفيدة في المستقبل ، مما يوفر أدلة على البحث المستقبلي في هذا المجال المحدد.
الکلمات المفتاحية: المعرفة ، الموقف ، الإنترنت ، الجنس ، والانضباط.
Abstract:
In Kuwait, teachers and educators must encourage students to use the Internet more for their own sake. However, I believe it is important first to explore students’ knowledge of and attitudes towards accepting and using the Internet, before asking them to use it. Most importantly, we need to determine the extent to which gender and discipline differences impact the acceptance and use of the Internet. Therefore, this study aims to explore knowledge of and attitudes towards the significance of the Internet, when endeavoring to enhance male and female students’ learning at (PAAET) in Kuwait. Furthermore, the impact of gender and discipline differences on such acceptance and use will also be explored at PAAET. The sample selected was a convenience sample, consisting of 30 (15 male and 15 female) pre-service teachers from different backgrounds (i.e., in terms of gender and discipline). A questionnaire was used to collect the data. The findings reveal that there were no differences related to students’ gender on levels of knowledge. However, there were differences related to discipline. Furthermore, the findings show differences between male and female students as regards their attitudes to using the Internet, with the difference in favour of male students. Although it is recognised that the range and generalisability of the study is limited, due to its small and non-representative sample, this study could be considered as an apt initial inroad into the study area, which could be useful in future, providing clues for future research in this specific field.
Keywords: Knowledge of, attitude, Internet, gender, and discipline.
1- INTRODUCTION
Globally, in the last twenty years, a great deal of attention has been drawn by educators and researchers towards using the Internet to support teaching and learning methods. However, despite the rising interest and use of the Internet by significant numbers of individuals in many educational institutions around the world, the use of the Internet by pre-service teachers at PAAET remains limited. This is mainly due to the many challenges. For instance, the availability of computers there is limited and not commensurate with the number of students. There is also a lack of maintenance, limited time for using and accessing the Internet and a lack of Wi-Fi services (Warf & Vincent, 2007). Working as a part time lecturer at College of Basic Education (CBE) in (PAAET) for two years made me aware of the deficiencies in the efficient use of the Internet in that environment.
Warf and Vincent (2007) have identified and explained some of the obstacles limiting the use of the Internet in the Arab region. One obstacle is the widespread predominance of English and corresponding use of the Latin alphabet in this domain. Further issues which have delayed the diffusion of the Internet include “low Arab literacy rates, and restrictive gender relations that keep the proportion of female users low” (Warf & Vincent, 2007, p. 92). Other factors retarding the spread of the Internet in the Arab world consist of the relatively high cost of Internet access (Sam et al., 2005). However, discussing such obstacles to use the Internet in the Arab region in greater detail and more extensively is outside the scope of this investigation.
One good point which could be made here is that adequate knowledge and positive attitudes are important factors for achieving an acceptable level of Internet use (Balka & Smith 2000). If students have poor knowledge, negative attitudes, or inaccurate perceptions of Internet use, it is expected they will face difficulties using it to serve their learning. This introduction is now followed by a brief review of previous studies on embedding the Internet in the lives of tertiary students.
2- LITERATURE REVIEW
Ogan et al., (2007), conducted a study in Indiana University, US, on embedding the Internet in the lives of tertiary students. The findings suggest that the university students concerned used the Internet more than four hours a day. All the students believed the Internet had a positive impact on their lives. For example, for news and information, 64% of the students declared that they preferred to consult Internet, rather than offline sources. In another study at a Malaysian University, Sam et al., (2005), carried out a study on mixed gender undergraduate students (i.e. 81 female and 67 male) to examine differences in levels of computer anxiety, computer self-efficacy and attitudes toward the Internet. One of the most significant findings is that the students used the Internet widely for educational purposes, e.g., e-mail communication, downloading E-resources and doing research. Furthermore, gender did not account for differences in Internet use patterns, computer self-efficacy, computer anxiety, or attitudes towards the Internet. The above authors challenged claims of the superiority of men over women in Internet use. Some researchers (Brosnan & Lee, 1998), (Balka, E. Smith, 2000) and (Sam et al., 2005), reported that males have more experience, engage in more computer use and are more self-confident about their computer skills than women are.
However, there are differences in students‟ usage levels which are based on the study discipline. For example, students from the Faculty of Computer Science and Information Technology (FCSIT) and the Faculty of Applied and Creative Arts (FACA) were found to use the Internet longer than other students. This shows that tertiary students in general, at least in this context, have sufficient knowledge and a positive attitude to using the Internet, despite their gender, but their area of study can have either a positive or negative effect on their Internet usage. Other researchers, (Li & Kirkup, 2007), investigated differences between Chinese and British students in their use of and attitudes to the Internet, alongside gender differences. They found that “Men played more computer games than women; Chinese men being the most active games players. Men in both countries were more self-confident about their computer skills than women and were more likely to express the opinion that using computers was a male activity and skill than were women” (Li & Kirkup, 2007, p. 303). The results from the last two studies mentioned are contradictory, with Sam et al. (2005), at least tacitly, failing to find any impact of gender on the acceptance and use of the Internet. Li & Kirkup (2007), however, claim the opposite. Hopefully, the result of this study could resolve such a contradiction and offer a clearer picture of the impact of gender differences on accepting and using the Internet. Regardless of the above, Peng et al. (2006), point out that university students generally have a positive attitude to the Internet and adequate Internet self-efficacy. They further suggest that students at this level view the Internet as a functional tool/technology. In their study, they report that male students have a more positive attitude to the Internet than their female peers. What is more, they indicate that those students who consider the Internet as a leisure tool demonstrate more positive attitudes and communicative self-efficacy than students who merely use the Internet as a functional tool. Regarding perceptions of the Internet, male students consider the Internet as a toy, while female students tend to consider it as a tool. Locally, in the same academic institution under study (PAAET), Alayyar, et al. (2009), found that 96% of participants (78 pre-service female science teachers in total) had an Internet connection at home and fewer (about 88%) had access to a computer in their department at PAAET. Most importantly, participants generally showed a positive attitude to Internet use, once they had received adequate support. However, it is questionable whether the male pre-service teachers at PAAET have the same facilities (e.g., enough computers and Internet access). It must be borne in mind that, in Kuwait, state-funded schools and higher academic institutions, like Kuwait University and PAAET, are forced by law to apply gender segregation.
3- AIMS OF THIS STUDY
The purpose of this small-scale enquiry is to explore knowledge of and attitudes to the significance of the Internet for enhancing male and female students‟ learning at PAAET in Kuwait. This study focuses on the following:
The Administration of Multi-item Instruments:
Utilising short multi-item questionnaire instruments, the constructs shall be operationalised as follows:
Knowledge of Internet issues: male and female students‟ ability to correctly answer 10 closed, multiple-choice questions, based upon the topic of the Internet and five questions with „right or wrong‟ answers. A high score will indicate high levels of knowledge of the significance of the Internet‟s impact on enhancing learning and a low score will indicate low levels of knowledge.
Attitudes to Internet-related issues: male and female students‟ self-reported responses to 14 statements regarding Internet-related issues. For each statement, the respondents were required to report their level of agreement on a five-point Likert scale (1=strongly agree, 2=agree, 3=neither agree nor disagree, 4=disagree, 5=strongly disagree).
Regarding the overall design of the questionnaire, it was self-administered and included open questions about background issues, as well as closed questions. The respondents were asked to circle or tick their answers from various options. Oppenheim (2000), indicates some advantages of these types of questions, such as their being quick to answer with no need to spend time writing; they are useful for testing specific hypotheses and the quantification is straightforward. However, there are disadvantages, such as a loss of spontaneity and expressiveness on the side of the respondents. As regards the administration of the questionnaire, it was provided in person and with the current class tutor. All participants were asked to complete the questionnaire independently and a brief description of the purpose of the study was presented, with instructions for filling out the questionnaire being made clear to the respondents.
Background Data:
The respondents‟ background data shall be collected on:
Questionnaire Development:
Hypotheses:
Each hypothesis refers to operationalised knowledge and attitudes, as mentioned above.
H1: There will be differences between students‟ genders on the level of knowledge concerning the significance of the Internet for learning.
H01: No differences will appear in relation to students‟ gender and their levels of knowledge of the significance of the Internet for learning.
H2: Students from different disciplines will demonstrate significant differences in their level of acceptance and use of the Internet for learning.
H02: No significant differences will occur between students from different disciplines as regards the acceptance and use of the Internet for learning.
H3: There will be differences between male and female students, related to their attitudes to using the Internet for learning.
H03: There are no differences between male and female students in their attitudes to using the Internet for learning.
4- METHODOLOGY
Measuring Instruments
The study adopted a descriptive methodology of inferential analysis and a questionnaire was distributed to a group of individuals to measure the hypothesis under study. Findings were drawn from the results of the questionnaire (see Appendix 1). The questionnaire, as the study‟s main instrument, comprised a number of questions about gender, discipline and other personal information on the respondents. It also included 14 items intended to measure behaviour and attitudes to the Internet amongst the sample, with these attitudes being classified using a five point Likert scale, as follows:
5= |
SA |
= I strongly agree with the statement |
4= |
A |
= I agree with the statement |
3= |
N |
= I neither agree nor disagree with the statement |
2= |
D |
= I disagree with the statement |
1= |
SD |
= I strongly disagree with the statement |
Moreover, in order to measure ‟knowledge‟, the questionnaire included 15items, in two sets of questions:
The first set asked the respondents to circle the letter indicating the correct answer, as each question was assigned four potential answers – only one being correct. There are 10 measurement items in this set.
The second set consists of statements that can be indicated as either true or false. This set includes five items.
Sample answers were checked (see Appendix 2). However, the correct answers for the 15 items – the total number of items contained in both the first and second set of questions - are presented in the Appendices. In short, this study instrument contains various variables (e.g., gender, discipline, attitudes, and knowledge).
5- SAMPLE
The selected sample consisted of 30 participants (15 male and 15 female), living in Kuwait and enrolled at PAAET as pre- service teachers. They had various background variables (gender, specialism). Obviously, the gender categories were just male and female, but specialisms varied between Special Education, Computer Science, Family Science, Arabic, Kindergarten Teaching and Islamic Studies. The students were in their first academic year and their ages at this stage normally range between 18 and 20 years old. It must also be mentioned that Educational Technology courses have more students than any other courses at PAAET, because they are general courses, on which many students from different specialisms need to enrol. Therefore, there can be as many as 50 or 60 participants for each group (male and female). Consequently, the questionnaire was distributed to students randomly, although this cannot be held as a random sample from which results can be generalized, as this study is a small scale empirical enquiry. Hence, it is considered as a convenience sample.
6- OUTLINE OF THE PROCEDURE
Data from the sample were entered after correction into the statistical programme SPSS, version 17 to conduct a proper analysis of the hypotheses being proposed. Appropriate statistical tests were conducted for each of the study's hypotheses.
Reliability and Validity:
Data validity was verified using a correlation coefficient between the scale for the ultimate degree of both attitudes = 14 items and knowledge scale = 15 items.
First - The Attitudes Scale (b)
TABLE I: RELIABILITY STATISTICS
Cronbach's Alpha |
No. of Items |
.279 |
14 |
TABLE II: ITEM-TOTAL STATISTICS
|
Scale Mean if Item Deleted |
Scale Variance if Item Deleted |
Corrected Item- Total Correlation |
Cronbach's Alpha if Item Deleted |
b1 |
47.9333 |
15.375 |
-.140- |
.365 |
b2 |
46.0333 |
13.482 |
.158 |
.236 |
b3 |
45.6667 |
14.713 |
.041 |
.278 |
b4 |
45.4667 |
14.257 |
.251 |
.236 |
b5 |
45.4667 |
15.223 |
-.080- |
.319 |
b6 |
45.8000 |
10.717 |
.556 |
.035 |
b7 |
47.5333 |
16.257 |
-.236- |
.398 |
b8 |
45.7667 |
13.357 |
.240 |
.211 |
b9 |
45.6333 |
15.757 |
-.160- |
.333 |
b10 |
46.0667 |
14.202 |
.046 |
.280 |
b11 |
45.9667 |
10.723 |
.503 |
.049 |
b12 |
46.4333 |
11.978 |
.178 |
.213 |
b13 |
47.9000 |
14.852 |
-.044- |
.313 |
b14 |
45.3000 |
14.769 |
.040 |
.278 |
However, it seems that the value of Cronbach's Alpha is too small, and this is due to the instability of some of the items in this scale. This requires deleting those suspect items from the scales because of their instability and re-conducting the analysis. The value of the items exceeded the total value of Cronbach's Alpha for the scale. Specifically, these items were b1-b5-b7-b13. After these items were excluded, the results were as follows:
TABLE III: RELIABILITY STATISTICS |
|
Cronbach's Alpha |
No. of Items |
.641 |
10 |
TABLE IV: ITEM-TOTAL STATISTICS
|
Scale Mean if Item Deleted |
Scale Variance if Item Deleted |
Corrected Item- Total Correlation |
Cronbach's Alpha if Item Deleted |
b2 |
35.8000 |
15.131 |
.307 |
.616 |
b3 |
35.4333 |
16.254 |
.268 |
.624 |
b4 |
35.2333 |
16.875 |
.268 |
.628 |
b6 |
35.5667 |
12.323 |
.684 |
.516 |
b8 |
35.5333 |
15.430 |
.334 |
.611 |
b9 |
35.4000 |
16.524 |
.204 |
.634 |
b10 |
35.8333 |
16.075 |
.164 |
.647 |
b11 |
35.7333 |
13.237 |
.485 |
.569 |
b12 |
36.2000 |
14.372 |
.200 |
.661 |
b14 |
35.0667 |
16.409 |
.258 |
.626 |
The above results are based on the total value of the stability coefficient being raised and the items after deletion being measured according to the purpose for which they were intended.
2 - Knowledge Scale (c):
TABLE V: RELIABILITY STATISTICS
Cronbach's Alpha |
No. of Items |
.691 |
15 |
TABLE VI: ITEM-TOTAL STATISTICS
|
Scale Mean if Item Deleted |
Scale Variance if Item Deleted |
Corrected Item - Total Correlation |
Cronbach's Alpha if Item Deleted |
c1 |
9.23333 |
6.392 |
.369 |
.666 |
c2 |
9.00000 |
6.690 |
.286 |
.678 |
c3 |
9.56667 |
8.116 |
-.324- |
.735 |
c4 |
9.03333 |
7.137 |
.090 |
.705 |
c5 |
9.60000 |
7.559 |
-.033- |
.706 |
c6 |
8.90000 |
5.955 |
.743 |
.619 |
c7 |
8.96667 |
6.033 |
.616 |
.632 |
c8 |
8.90000 |
6.852 |
.272 |
.679 |
c9 |
8.90000 |
6.093 |
.666 |
.629 |
c10 |
9.13333 |
6.189 |
.460 |
.652 |
c11 |
8.76667 |
6.737 |
.604 |
.656 |
c12 |
8.86667 |
6.257 |
.630 |
.637 |
c13 |
8.93333 |
7.375 |
.016 |
.710 |
c14 |
9.00000 |
6.414 |
.409 |
.660 |
c15 |
9.00000 |
7.655 |
-.107- |
.728 |
It may be noted that, on the knowledge scale, the value of consistency is 69.1%, which is good, but there were some items with a value which was greater than the total value of the stability and this required them to be deleted and the test to be re-conducted. These items were c3-c5-c4-c15-c13. After reiterating the analysis, it appears that the total value of the coefficient Alpha is 82.8%.
TABLE VII: RELIABILITY STATISTICS
Cronbach's Alpha |
No. of Items |
.828 |
10 |
TABLE VIII: ITEM-TOTAL STATISTICS
|
Scale Mean if Item Deleted |
Scale Variance if Item Deleted |
Corrected Item - Total Correlation |
Cronbach's Alpha if Item Deleted |
c1 |
6.8667 |
6.120 |
.353 |
.832 |
c2 |
6.6333 |
6.102 |
.410 |
.823 |
c6 |
6.5333 |
5.637 |
.757 |
.788 |
c7 |
6.6000 |
5.766 |
.600 |
.803 |
c8 |
6.5333 |
6.395 |
.342 |
.828 |
c9 |
6.5333 |
5.706 |
.717 |
.792 |
c10 |
6.7667 |
5.840 |
.480 |
.817 |
c11 |
6.4000 |
6.386 |
.635 |
.810 |
c12 |
6.5000 |
5.845 |
.696 |
.796 |
c14 |
6.6333 |
6.171 |
.378 |
.827 |
The above results are based on the total value of the stability coefficient being raised and items after deletion being measured according to the purpose for which they were intended. The number of items comprised in this scale is 10.
7- ETHICAL ISSUES
When conducting any research, especially in the humanistic sciences, there will always be ethical issues. These issues relate to the study participants, who, in this case, were all mature and understood the purpose of the study and the questionnaire from an explicit statement presented to them in written form and attached to the questionnaire (see Appendix 1). According to the Ethical Guidelines for Educational Research (BERA, 2011), it is crucial that all research must ensure permission from the participants to reveal their personal information. Furthermore, it is good practice to debrief the participants on the outcomes at the conclusion of the research. Consequently, it was made clear to the participants that they had the right to ask to see the correct answers after finishing the study. Another significant issue is the participants‟ entitlement to privacy (which is applied in this study). This implies the 'confidential and anonymous treatment of their data'(BERA, 2011) . The questionnaire distributed in this study did not ask the participants about their identity and were designated numerical codes for administration purposes. All data were handled with care all the way through and remained secure and confidential, as prescribed by the Data Protection Act (1998), with the researcher being the only one with access to them. Moreover, all participants were made aware that all copies of the questionnaire would be destroyed once the study was finished, as recommended by (Bell, 2005).
8- THE RESULTS
Based on the „Test of Normality‟, it was found that the value of sig. is greater than 0.05. Therefore, it could be said that the data has normal distribution in this case. However, if the value of sig. – in the case of the knowledge scale – is greater than 0.05, then data distribution is not normal, as its value is 0.000. On the other hand, in the case of the attitude scale, the value of sig. is greater than 0.05 and this indicates that the distribution is normal. See TABLES IX and X.
TABLE IX: KNOWLEDGE TESTS OF NORMALITY
Tests of Normality |
||||||
|
Kolmogorov-Smirnova |
Shapiro-Wilk |
||||
Statistic |
df |
Sig. |
Statistic |
df |
Sig. |
|
Total Knowledge |
.243 |
30 |
.000 |
.883 |
30 |
.003 |
aLilliefors Significance Correction |
TABLE X: ATTITUDE/TESTS OF NORMALITY
TESTS OF NORMALITY
|
Kolmogorov-Smirnova |
Shapiro-Wilk |
||||
Statistic |
df |
Sig. |
Statistic |
df |
Sig. |
|
Total Attitude |
.126 |
30 |
.200* |
.977 |
30 |
.733 |
aLilliefors Significance Correction
*This is a lower bound of the true significance
By examining the Graph, it seems apparent that the data are concentrated around the straight line. Therefore, it is distributed according to the normal distribution for both scales: the attitude scale and the knowledge scale. Nonetheless, some data are lying further away on the scale of knowledge (see Graphs I and II).
GRAPH I
GRAPH 2
Since the sample consisted of 30 persons or less, then the test statistics are „T‟, in the sense that we will use and conduct the T-test.
Hypothesis 1:
H1: There will be differences between students‟ genders on the level of knowledge concerning the significance of the Internet for learning.
H01: No differences will appear in relation to students‟ gender and their levels of knowledge of the significance of the Internet for learning.
Results: To explore the results of the hypothesis, the Independent Samples Test was used and the results are as shown in TABLE XI and XII:
TABLE XI: TOTAL KNOWLEDGE SCORE FOR MALES AND FEMALES
Group Statistics
Gender |
N |
Mean |
Std. Deviation |
Std. Error Mean |
|
Total Knowledge Score |
Male |
15 |
8.8667 |
3.09069 |
.79801 |
|
Female |
15 |
10.5333 |
2.16685 |
.55948 |
Independent Samples Test
TABLE XII: INDEPENDENT SAMPLES TEST
|
Levene's Test for Equality of Variances |
T-test for Equality of Means |
||||||||
|
95% Confidence Interval of the Difference |
|||||||||
F |
Sig. |
T |
df |
Sig. (2- tailed) |
Mean Difference |
Std. Error Differ ence |
Lower |
Upper |
||
Total Knowledge |
Equal variances assumed |
1.758 |
.196 |
-1.710- |
28 |
.098 |
-1.66667E0 |
.97460 |
-3.66304- |
.32971 |
|
Equal variances not assumed |
-1.710- |
25.085 |
.100 |
-1.66667E0 |
.97460 |
-3.67355- |
.34021 |
According to XI, which illustrates the value of the arithmetic mean (M) and standard deviation (SD) for both males (N =
15) and females (N = 15), the mean for males is 8.88 and the standard deviation is 3.09, while the mean for females is
10.53 and the standard deviation is 2.16. In addition, based on TABLE XII, which shows the results of the Independent Samples Test, it seems that the value of „T‟ is 1.710, which is not statistically significant at a level of less than or equal to 0.05. Hence, it can be concluded that:
No differences will occur related to students‟ gender on the levels of knowledge of the significance of the Internet for learning, and therefore, H01 is accepted and H1 is rejected.
Hypothesis 2:
H2: Students from different disciplines will demonstrate significant differences in their level of acceptance and use of the Internet for learning.
H02: No significant differences will occur between students from different disciplines as regards the acceptance and use of the Internet for learning.
TABLE XIII: ONE-WAY ANOVA.
ANOVA
Total Knowledge
|
Sum of Squares |
df |
Mean Square |
F |
Sig. |
Between Groups |
67.060 |
4 |
16.765 |
2.735 |
.05 |
Within Groups |
153.240 |
25 |
6.130 |
||
Total |
220.300 |
29 |
|
TABLE XIV: LCD test
Multiple
Comparisons Total
Knowledge LSD
|
|
Mean Difference (I-J) |
Std. Error |
Sig. |
95% Confidence Interval |
|
(I) a2 Specialism |
(J) a2 specialism |
Lower Bound |
Upper Bound |
|||
Computer Sc. |
Special Ed. |
-2.25000- |
1.59813 |
.171 |
-5.5414- |
1.0414 |
|
Family Science |
-.58333- |
1.59813 |
.718 |
-3.8747- |
2.7081 |
|
Arabic Lang. |
-3.72619-* |
1.17748 |
.004 |
-6.1513- |
-1.3011- |
|
Islamic Studies |
-.78333- |
1.31785 |
.558 |
-3.4975- |
1.9308 |
Special Ed. |
Computer Sc. |
2.25000 |
1.59813 |
.171 |
-1.0414- |
5.5414 |
|
Family Science |
1.66667 |
2.02149 |
.417 |
-2.4967- |
5.8300 |
|
Arabic Lang. |
-1.47619- |
1.70847 |
.396 |
-4.9948- |
2.0425 |
|
Islamic Studies |
1.46667 |
1.80807 |
.425 |
-2.2571- |
5.1905 |
Family Science |
Computer Sc. |
.58333 |
1.59813 |
.718 |
-2.7081- |
3.8747 |
|
Special Ed. |
-1.66667- |
2.02149 |
.417 |
-5.8300- |
2.4967 |
|
Arabic Lang. |
-3.14286- |
1.70847 |
.078 |
-6.6615- |
.3758 |
|
Islamic Studies |
-.20000- |
1.80807 |
.913 |
-3.9238- |
3.5238 |
Arabic Lang. |
Computer Sc. |
3.72619* |
1.17748 |
.004 |
1.3011 |
6.1513 |
|
Special Ed. |
1.47619 |
1.70847 |
.396 |
-2.0425- |
4.9948 |
|
Family Science |
3.14286 |
1.70847 |
.078 |
-.3758- |
6.6615 |
|
Islamic Studies |
2.94286 |
1.44968 |
.053 |
-.0428- |
5.9285 |
Islamic Studies |
Computer Sc. |
.78333 |
1.31785 |
.558 |
-1.9308- |
3.4975 |
|
Special Ed. |
-1.46667- |
1.80807 |
.425 |
-5.1905- |
2.2571 |
|
Family Science |
.20000 |
1.80807 |
.913 |
-3.5238- |
3.9238 |
|
Arabic Lang. |
-2.94286- |
1.44968 |
.053 |
-5.9285- |
.0428 |
*The mean difference is significant at 0.05
TABLE XIII demonstrates an analysis of the variance (one-way ANOVA) used to measure the hypothesis, based on two variables: knowledge and discipline. The analysis shows that the value of „f‟ is statistically significant at a level less than or equal to 0.05 and so it may be concluded that there is a difference related to the study discipline.
More specifically, conducting post-hoc comparisons using LCD tests (see TABLE XIV) between the groups, where different disciplines are concerned (e.g., Computer Science, Special Education, Family Science, Arabic Language) revealed statistically significant differences between the groups, with the Mean Difference between those studying Computer Science and Arabic Language being 3.72. This difference is statistically significant at a level less than or equal to 0.05 and is in favour of the Arabic Language group. Therefore, considering this difference, hypothesis H2 is accepted, while hypothesis H02 is rejected.
Hypothesis 3:
H3: There will be differences between male and females related to their attitudes to using the Internet for learning. H03: There are no differences between males and females related to their attitude to using the Internet for learning.
TABLE XV: TOTAL 5 KNOWLEDGE SCORE FOR MALES AND FEMALES
Group Statistics
a1 Gender |
N |
Mean |
Std. Deviation |
Std. Error Mean |
|
Total Attitude |
Male |
15 |
51.2000 |
3.93156 |
1.01512 |
|
Female |
15 |
48.3333 |
3.45722 |
.89265 |
TABLE XVI: INDEPENDENT SAMPLES TEST
Independent Samples Test
|
Levene's Test for Equality of Variances |
T-test for Equality of Means |
||||||||
|
95% Confidence Interval of the Difference |
|||||||||
F |
Sig. |
T |
df |
Sig. (2- tailed) |
Mean Difference |
Std. Error Difference |
Lower |
Upper |
||
Total Attitude |
Equal variances assumed |
.560 |
.461 |
2.121 |
28 |
.043 |
2.86667 |
1.35178 |
.09768 |
5.63566 |
|
Equal variances not assumed |
2.121 |
2.755E1 |
.043 |
2.86667 |
1.35178 |
.09564 |
5.63770 |
According to TABLE XV, which illustrates the value of the arithmetic mean (M) and standard deviation (SD) for both males (N = 15) and females (N = 15), the mean for males is 51.2 and the standard deviation is 3.93, while for females, the arithmetic mean is 48.33 and the standard deviation is 3.45.
Based on TABLE XVI, which shows the results of the Independent Samples Test, it seems that the value of „T‟ is 2.12, which is statistically significant at a level of less than or equal to 0.05. Hence, it can be concluded that:
There will be differences between males and females related to their attitudes to using the Internet for learning and this difference is in favour of males as the Mean Difference is 2.86 and, in this case, the hypothesis H3 is accepted and the hypothesis H03 is rejected.
9- DISCUSSION
The results of the data reveal numerous findings. First, this investigation illustrates that there are no differences related to students‟ gender on the levels of knowledge of the significance of the Internet for learning. Luan et al., (2005), support these findings in their study, where male and female student teachers‟ reactions to the Internet were sought. They found that there was no gender disparity in Internet usage.
The study also investigated the differences in the impact of disciplines on the acceptance and use of the Internet. The findings show that there is a difference related to the type of discipline. For instance, there are statistically significant differences between the Computer Science and Arabic Language groups, as the Mean Difference between them is 3.72 and this difference is statistically significant at a level less than or equal to 0.05, in favour of the Arabic Language group. These findings need deeper investigation for clarification. This could be achieved by preparing a new set of questions and interviewing a random sample of students specialising in Arabic Language and another specialising in Computer Science, to try and understand this bizarre result. Another investigation conducted in this study was amongst male and female students, in relation to their attitudes to using the Internet for learning. The result shows a difference in favour of males, as the Mean Difference is 2.86. These findings reaffirm some of the studies mentioned earlier (Peng et al., 2006) and (Alayyar, et al., 2009).
10- LIMITATIONS
This study comes with a number of limitations which have major implications on generalisability and confidence in the findings. These limitations refer to the sample size and the instrument used to generate the data. Due to time restrictions, I could not use another instrument, such as an interview or direct observation to investigate the findings in depth. This small scale empirical enquiry could therefore be considered as an exploratory phase, which would allow for another community of practice (e.g. teachers, educators, researchers and policy-makers) at PAAET to comprehend the possible impact of gender and other principle differences on students‟ attitudes to the Internet.
11- CONCLUSION
Owing to the relatively small sample size, it was not possible to validate the instruments properly. Additional research is therefore needed, using larger samples and another instrument (e.g., interview), which will allow the instrument to be validated as thoroughly as possible and justify some of the strange findings which emerged. The data suggests that, in addition to using the Internet as a source of information, students also use it as a tool of communication. As noted above, there are no differences between tertiary students as regards knowledge of the Internet, while amongst the background variables (such as discipline) statistically significant differences emerged. Moreover, unsurprising findings came out from the data, which showed that males have a better attitude to the Internet than females and this finding is supported in a great deal of another research.
Noticeably, many students in academic institutions around the world rely on the Internet or prefer doing research for their projects on the Internet, rather than visiting the library. They consider the Internet to be the fastest way of obtaining information. Moreover, many of them have been found to agree they should be given projects based on the Internet (see for example, Gordon et al. (2007), Hong et al. (2007), and Englander et al. (2010). Hence, it is recommended that teachers at PAAET encourage their students when assigning tasks that involve Internet use. Discovering learners‟ knowledge and attitudes towards any new method, especially the Internet, is not an easy job, but is nevertheless vital to successful integration. Students therefore need to be trained, not only in skills such as scanning, skimming, and eliminating unnecessary information, but also in how to overcome obstacles that might prevent them from using technology (the Internet) to enhance their learning. More research in this field is needed, especially at PAAET, to understand the perspectives of both teachers and students on the Internet.
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