4 From a methodologically rigorous point of view it is not allowed to calculate an average of ordinally scaled data (though when summing up a set of items, it is quite common). Nevertheless, we took averages because the values show interesting trends.
Table 2
Use of E-Learning-Tools for Different Activities Separately for University
Use of e-learning-tools for
…
Literature search
Query the library account /
Mark of books
Access to online information tools (e.g., online journals) Download of event
materials
Register to events
Access to the lecture directory
Chats to communicate with students
Chats to communicate with lecturers / tutors
E-mail communication with fellow students,
tutors, and lecturers
Online lectures
Online educational offers
for self-studies
Online seminars
Formation of working groups
Access to student-
organized platforms
Online solving tasks
Average of all services
Note: Self-disclosures from 1 = I use regularly and often to 4 = I have never done. The differences in the average were tested on a 97.5 percent-interval for significance; “sg” means differences are significant in the average to B = HTW Berlin; F = Uni Freiburg; Z = Uni Zurich.
Table 3
Use of E-Learning-Tools for Different Activities Separately by Sex
Use of e-learning-tools for …
Female
Male All students
Literature search 2.27 sg
2.57
2.41
Query the library account /
mark of books 2.53 sg
2.93
2.72
Access to online information tools (e.g.
online journals)
2.53
2.65
2.59
Download of event materials 1.35 sg
1.61
1.48
Register to events 1.37 sg
1.55
1.46
Access to the lecture directory 1.47 sg
1.68
1.57
Chats to communicate with students 2.8 sg
2.96
2.88
Chats to communicate with lecturers /
tutors 3.16 sg
3.31
3.23
E-mail communication with fellow students, tutors and lecturers
2.43
2.57
2.5
Online lectures 2.77 sg
3.07
2.91
Online educational offers for self- studies
2.56
2.71
2.63
Online seminars
3.57
3.5
3.54
Formation of working groups
3.37
3.41
3.39
Access to student-organized platforms
3.14
3.13
3.13
Online solving tasks
2.56 sg
2.87
2.71
Average of all services
2.53
2.7
Note: Self-disclosures from 1 = I use regularly and often to 4 = I have never done. The differences in the average were tested on a 97.5 percent-interval for significance; “‘sg”’ means differences by female / male are significant in the average.
It was also remarkable that on average the female students assessed themselves as being more active in the usage of e-learning than their male counterparts. Based on our general hypothesis, this may be explained by the fact that e-learning in higher education is understood as learning rather than technology, thus fitting even more easily into female self-concepts.
In a further part of the questionnaire, students had to answer questions concerning their computer skills. Using Cronbach’s alpha for these questions, we developed 21 items which we divided into the three variables: competency in standard software, competency in media design, and computer skills mastery. 5 The given items were for example: I have a good overview of the data on my computer; I am able to arrange documents and essays in an attractive way by using a word processor; I find it easy to solve computer problems. The students were able to respond with 1 (is not the case), 2 (is rather not the case), 3 (is rather the case), and 4 (is the case), so that conformity in the general format signified relevantly high self-confidence. The three clusters extracted from the results showed the following summary results: Concerning competency in
5 The competency in standard software was determined by the following items:
- I have a good overview of the data on my computer.
- I am able to effectively protect my computer from viruses and hackers.
- I am able to create essays by using attractive and convenient word processing programmes.
- I am able to make a well-prepared, computer-based presentation of attractive design.
- I am able to process and visualize by using spreadsheet numerical data.
- I am able to send e-mails with attached files to one or more persons using an e-mail programme.
- I am able to find the information I am searching for quickly by using the internet.
- I am able to further process by using image-processing programme, existing images or photos.
The competency in media design was determined by the following items:
- I am able to create by using graphics programmes, clear diagrams, attractive invitations or posters.
- I am able to take, cut and edit by using audio software sounds, language or music, so as to
create an attractive audio track.
- I am able to cut and edit by using video editing software digital videos, so as to create an attractive video track.
- I am able to burn CDs and DVDs by using burning software and to create matching cover and
stickers.
- I am able to create web pages attractively and clearly and to publish the pages in the internet.
- I am able to write smaller programmes in at least one programming language.
And the variable computer skills mastery was determined by the following items:
- I find it easy to understand new working methods with the computer, and to understand new programmes.
- I think I can solve problems that might arise while working with the computer.
- I still believe I have a good competence level of computer usage even after experiencing a time of failure during usage.
- I have a good feeling when it comes to my computer skills.
- I can change settings (for example system settings) on the computer by myself and also customize, without having to consult anyone.
- I find it easy to solve computer problems.
- I think that I am good at explaining a computer programme to others.
standard software there was no significant difference between the sexes. Around 98% of male students and around 96% of their female fellow students thought that they were (rather) competent in this field. Regarding the variable competency in media design, students’ answers aggregated as follows: More than 50% of male students but only 27% of their female fellow students considered themselves (rather) competent. Finally, concerning computer skills mastery, 87% of male students assumed themselves to be (rather) competent but only 66% of female students did so.
These results can be interpreted in line with our hypothesis as follows: In everyday routinized activities (including everyday problems) computers and the Internet are merely tools with no outstanding technological appeal and are thus no longer suitable for differential gender performance. However, when the activities and applications are no longer part of everyday routine and problems appear to lack transparency and to be uncontrollable, ICT again becomes a gender biased technology suitable for expressing gender differences. Consequently, computer buffs and nerds are still typically male.
Using a little interpretational boldness, further interesting results can be found in this data. We asked questions on the diversity of computer and internet usage and the answers revealed the above-mentioned results. On that basis, we examined the diversity of computer use in regard to correlations between/with the three competence classes. For this purpose, diversity of use was split into five categories. The first category included those students who use the computer with maximum diversity, while the fifth category included the students who use the computer with minimal diversity. The second to fourth categories were the gradations in between. We also categorized the three self-rated skills into four categories. Here, the first category included students who assessed their skills as being very low while the fourth category included students who assessed their skills as being very high. The second and third categories were the gradations in between. They showed the following results: Spearman correlation, that is, the coefficient measuring the strength of the correlation, between diversity of computer use and competency in standard software, 0.345; 6 Spearman correlation between diversity of computer use and competency in media design, 0.366; Spearman correlation between diversity of computer use and computer skills mastery, 0.424. Not surprisingly, there was overall a mild to medium correlation between the diversity of computer use and the self-rated computer skills since these items can be seen as being mutually related. This means, if the user assesses his competence high, he also assesses his diversity of use high.
However, viewing this from our basic assumption regarding the importance of self- assessment of competences for usage of technology, the data shows an interesting differentiation in relation to gender: Spearman correlation between diversity of use and competency in standard software is 0.417 (female) and 0.300 (male). Spearman
6 The correlation has a negative sign because of the categorization in the diversity (the value for high diversity is 1, the value for low diversity is 2. In comparison to this, a low self-rated skill = 1, and a high self-rated skill = 4). We have omitted the sign for better understanding.
correlation between diversity of use and competency in media design is 0.381 (female) and 0.282 (male). Spearman correlation between diversity of use and computer skills mastery is 0.443 (female) and 0.351 (male). So correlation between the self-assessed competences and the diversity of usage for female students is always slightly stronger than for the male students. Of course, this small difference could be considered as not particularly noteworthy. However, based on Hagemann-White’s (1993) assumption that the regular male behavior is dominant and a part of the co-construction of gender and technology, the slight twist in the data makes sense. Consequently, men ju
4 From a methodologically rigorous point of view it is not allowed to calculate an average of ordinally scaled data (though when summing up a set of items, it is quite common). Nevertheless, we took averages because the values show interesting trends.
Table 2
Use of E-Learning-Tools for Different Activities Separately for University
Use of e-learning-tools for
…
Literature search
Query the library account /
Mark of books
Access to online information tools (e.g., online journals) Download of event
materials
Register to events
Access to the lecture directory
Chats to communicate with students
Chats to communicate with lecturers / tutors
E-mail communication with fellow students,
tutors, and lecturers
Online lectures
Online educational offers
for self-studies
Online seminars
Formation of working groups
Access to student-
organized platforms
Online solving tasks
Average of all services
Note: Self-disclosures from 1 = I use regularly and often to 4 = I have never done. The differences in the average were tested on a 97.5 percent-interval for significance; “sg” means differences are significant in the average to B = HTW Berlin; F = Uni Freiburg; Z = Uni Zurich.
Table 3
Use of E-Learning-Tools for Different Activities Separately by Sex
Use of e-learning-tools for …
Female
Male All students
Literature search 2.27 sg
2.57
2.41
Query the library account /
mark of books 2.53 sg
2.93
2.72
Access to online information tools (e.g.
online journals)
2.53
2.65
2.59
Download of event materials 1.35 sg
1.61
1.48
Register to events 1.37 sg
1.55
1.46
Access to the lecture directory 1.47 sg
1.68
1.57
Chats to communicate with students 2.8 sg
2.96
2.88
Chats to communicate with lecturers /
tutors 3.16 sg
3.31
3.23
E-mail communication with fellow students, tutors and lecturers
2.43
2.57
2.5
Online lectures 2.77 sg
3.07
2.91
Online educational offers for self- studies
2.56
2.71
2.63
Online seminars
3.57
3.5
3.54
Formation of working groups
3.37
3.41
3.39
Access to student-organized platforms
3.14
3.13
3.13
Online solving tasks
2.56 sg
2.87
2.71
Average of all services
2.53
2.7
Note: Self-disclosures from 1 = I use regularly and often to 4 = I have never done. The differences in the average were tested on a 97.5 percent-interval for significance; “‘sg”’ means differences by female / male are significant in the average.
It was also remarkable that on average the female students assessed themselves as being more active in the usage of e-learning than their male counterparts. Based on our general hypothesis, this may be explained by the fact that e-learning in higher education is understood as learning rather than technology, thus fitting even more easily into female self-concepts.
In a further part of the questionnaire, students had to answer questions concerning their computer skills. Using Cronbach’s alpha for these questions, we developed 21 items which we divided into the three variables: competency in standard software, competency in media design, and computer skills mastery. 5 The given items were for example: I have a good overview of the data on my computer; I am able to arrange documents and essays in an attractive way by using a word processor; I find it easy to solve computer problems. The students were able to respond with 1 (is not the case), 2 (is rather not the case), 3 (is rather the case), and 4 (is the case), so that conformity in the general format signified relevantly high self-confidence. The three clusters extracted from the results showed the following summary results: Concerning competency in
5 The competency in standard software was determined by the following items:
- I have a good overview of the data on my computer.
- I am able to effectively protect my computer from viruses and hackers.
- I am able to create essays by using attractive and convenient word processing programmes.
- I am able to make a well-prepared, computer-based presentation of attractive design.
- I am able to process and visualize by using spreadsheet numerical data.
- I am able to send e-mails with attached files to one or more persons using an e-mail programme.
- I am able to find the information I am searching for quickly by using the internet.
- I am able to further process by using image-processing programme, existing images or photos.
The competency in media design was determined by the following items:
- I am able to create by using graphics programmes, clear diagrams, attractive invitations or posters.
- I am able to take, cut and edit by using audio software sounds, language or music, so as to
create an attractive audio track.
- I am able to cut and edit by using video editing software digital videos, so as to create an attractive video track.
- I am able to burn CDs and DVDs by using burning software and to create matching cover and
stickers.
- I am able to create web pages attractively and clearly and to publish the pages in the internet.
- I am able to write smaller programmes in at least one programming language.
And the variable computer skills mastery was determined by the following items:
- I find it easy to understand new working methods with the computer, and to understand new programmes.
- I think I can solve problems that might arise while working with the computer.
- I still believe I have a good competence level of computer usage even after experiencing a time of failure during usage.
- I have a good feeling when it comes to my computer skills.
- I can change settings (for example system settings) on the computer by myself and also customize, without having to consult anyone.
- I find it easy to solve computer problems.
- I think that I am good at explaining a computer programme to others.
standard software there was no significant difference between the sexes. Around 98% of male students and around 96% of their female fellow students thought that they were (rather) competent in this field. Regarding the variable competency in media design, students’ answers aggregated as follows: More than 50% of male students but only 27% of their female fellow students considered themselves (rather) competent. Finally, concerning computer skills mastery, 87% of male students assumed themselves to be (rather) competent but only 66% of female students did so.
These results can be interpreted in line with our hypothesis as follows: In everyday routinized activities (including everyday problems) computers and the Internet are merely tools with no outstanding technological appeal and are thus no longer suitable for differential gender performance. However, when the activities and applications are no longer part of everyday routine and problems appear to lack transparency and to be uncontrollable, ICT again becomes a gender biased technology suitable for expressing gender differences. Consequently, computer buffs and nerds are still typically male.
Using a little interpretational boldness, further interesting results can be found in this data. We asked questions on the diversity of computer and internet usage and the answers revealed the above-mentioned results. On that basis, we examined the diversity of computer use in regard to correlations between/with the three competence classes. For this purpose, diversity of use was split into five categories. The first category included those students who use the computer with maximum diversity, while the fifth category included the students who use the computer with minimal diversity. The second to fourth categories were the gradations in between. We also categorized the three self-rated skills into four categories. Here, the first category included students who assessed their skills as being very low while the fourth category included students who assessed their skills as being very high. The second and third categories were the gradations in between. They showed the following results: Spearman correlation, that is, the coefficient measuring the strength of the correlation, between diversity of computer use and competency in standard software, 0.345; 6 Spearman correlation between diversity of computer use and competency in media design, 0.366; Spearman correlation between diversity of computer use and computer skills mastery, 0.424. Not surprisingly, there was overall a mild to medium correlation between the diversity of computer use and the self-rated computer skills since these items can be seen as being mutually related. This means, if the user assesses his competence high, he also assesses his diversity of use high.
However, viewing this from our basic assumption regarding the importance of self- assessment of competences for usage of technology, the data shows an interesting differentiation in relation to gender: Spearman correlation between diversity of use and competency in standard software is 0.417 (female) and 0.300 (male). Spearman
6 The correlation has a negative sign because of the categorization in the diversity (the value for high diversity is 1, the value for low diversity is 2. In comparison to this, a low self-rated skill = 1, and a high self-rated skill = 4). We have omitted the sign for better understanding.
correlation between diversity of use and competency in media design is 0.381 (female) and 0.282 (male). Spearman correlation between diversity of use and computer skills mastery is 0.443 (female) and 0.351 (male). So correlation between the self-assessed competences and the diversity of usage for female students is always slightly stronger than for the male students. Of course, this small difference could be considered as not particularly noteworthy. However, based on Hagemann-White’s (1993) assumption that the regular male behavior is dominant and a part of the co-construction of gender and technology, the slight twist in the data makes sense. Consequently, men ju
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