Evaluating The College Students Likelihood Of Knowledge Retention Education Essay

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With the generalized use of Information Technologies (IT), students gather and use information from the World Wide Web and use it to complete their schoolwork and other related tasks. However, there are no indications that any of the obtained information is actually have been critically thought about, or if the material was understood by the student. While the use of the Internet is important for acquiring knowledge, however, students' attitudes are also as important.

This study will help better understand the students' intentions to use the Internet, and enable educators to determine the value of the retained knowledge. Although limited by a small students' participant sample, the findings and the conclusions appear justified. These results highlight areas for further research and provide a basis for institutions to consider these associations in future policy or plan formulation for relying on the Internet as an education channel. The research suggested that further research should be conducted to monitor the affect of using the Internet on the learning and knowledge retention of the youngest age students group.

Keywords: knowledge Retention, college students, Information, the Internet

Introduction

The internet revolution led to a new generation of college students, where most of them have grown up with technology. Many had their first experience with computers early on in their school years. 30-40 % of youngster (youth in the age range of 8-14 years) are internet users (Geyer, 2009). This correlates with the educators' directions to consider the Internet as a new medium for education. In addition, institutions strongly encourage the instructors to adapt information technologies in their regular teaching. Therefore, the advancement of information technology and the easy access of computers are changing the educational platform, resulting in a widespread use of the internet as an education medium by institutions for both online and traditions face-to-face learning. Furthermore, the growing use of the Internet as a source of information has affected the learning capabilities and knowledge retention.

The Internet offers students the possibility to acquire knowledge without time and space constraints. Nevertheless, the added value of the Internet should be viewed in the learning and knowledge retention, and not in the instrument used to vehicle new contents and new concepts. This study will examine the use of the Internet for learning and acquiring knowledge through the analysis of item attributes and determine the variables affecting college students' likelihood to retain the knowledge. This study will identify the variables that shape and influence the students' likelihood to use the Internet as a source of information, extract knowledge, and retain the acquired knowledge, and then investigates whether there is a relationship between the students' intention to use the Internet as a source of information and their actual learning and knowledge retention.

Significance of the study

Students gather and use information from digital online documents and web pages to complete schoolwork and other related tasks. Students obtaining information online have to know how to find, use, and share information for learning. Since students have to deal with a huge amount of information, acquiring information and extracting it into knowledge seems to be necessary to achieve. While knowledge building is not limited to education, however, the approach requires engaging learners in the full process of knowledge creation(Scardamalia and Bereiter, 2003). Knowing perception of the usefulness of using the Internet for acquiring and retaining knowledge is very important to know and will help the traditional face-to-face as well as online educators. The importance of learning or continuous improvement is placed in the knowledge base and institutional arrangements for development (Conceição, et al, 2003). A study compared face-to-face teaching with a hybrid delivery and found one major difference, and that students perceived online learning to be more difficult (Senn, 2008). But it is unclear as to the affects that a greater online education system will have on our society as a whole (Fish, and Gill, 2009). Therefore, learning and knowledge retention should not be assumed when someone, who does not know, accesses information that was produced by someone who knows, and the educational benefit needs to be focused on the process of acquiring and retaining knowledge.

Literature Review

With the generalized use of Information Technologies (IT), and mainly with appearance of World Wide Web (Web), learning and knowledge acquisition have upgraded to the new digital age, supported by electronic means, commonly defined as electronic learning (e-learning). Online electronic education is now being widely accepted as a major viable component of higher education (Junaidu, 2008). However, the definition of e-learning is being used to assign very diverse realities that ranges from the linear transposition for Web to the development of innovative proposals like the creation of virtual communities of learning (Almala, 2004). Learning is an important component of any institution that cannot be taken for granted by simply developing a high quality document that was based on the collection of information from different website. Learning has been defined in terms of knowledge (Mayer, 1987), or knowledge and observable behavior (Slavin, 1994) and should result in a positive change that improves students' capabilities, skills, or knowledge.

Since the concept was first introduced, Internet usage for education substantially increasing, and many institutions are using it for distance-learning programs and connecting their academic staff to improve teaching and research (Osunade, et al, 2009) . Yet there is little literature evaluating the effectiveness of learning in such environment compared to the traditional face-to-face classroom (Garrison, and Schardt, 2007). Some studies have analyzed how the Internet can be used as a useful tool to improve teaching, and thus improve student's academic performance (Muñoz and Montoliu, 2008). Other studies evaluated the quality of e-learning in institutions of higher education and examined the implementation stages of this learning process (Almala, 2004). Generally students that do the best are those who might need time to think and reflect before they respond to questions and concepts (Solimeno, et al, 2008) .

Carefully selected instructional strategies increase student participation in online courses rather than assigning grades (Brindley et al, 2009). Some of the challenges that instructors faced with in teaching online is the need for professional development to stay current with the technologic needs of online teaching (Conrad and Pedro, 2009). Students consistently perform much better in assignments requiring application of material taught in carefully animated algorithms (Junaidu, 2008). A number of barriers to student learning outcomes and delivery of academic tasks online are perceived by the faculty (Fish, and Gill 2009). Age and gender play a major role in electronic learning. Female students have a better perception of online education and that the older students preferred face to face classrooms to online education (Dabai, 2009), and students who use the online homework assignments and spend more time doing homework can improve their overall scores on exams (Demirci, 2007).

In light of that, searching for information on the Internet does not necessarily mean that some kind of learning process is going on, especially when doing an assignment (or other university work), that can be done by looking for information on the web, and then "copy and paste" the suitable ones. Consequently, it's a copy and paste activity with almost no cognitive effort (Donoso and Roe, 2006). For such a reason using internet by students for learning purposes should be like any other tool, it must be used correctly, otherwise the results may be the opposite of what is desired (Muñoz and Montoliu, 2008). Therefore, learning and knowledge retention should not be assumed, because someone (who does not know) accessed the

information that was produced by someone who knows.

Research Hypotheses and Methodology

The main objective of this study is to try and identify the variables that shape and influence the students' likelihood to use the Internet as a source of information, extract knowledge, and retain the acquired knowledge. In addition, we will investigates whether there is a relationship between the students' intention to use the Internet as a source of information and their actual learning and knowledge retention. A survey is developed and distributed among graduate and undergraduate students in four different universities. The following hypotheses have been developed:

H1: Students do not have a negative attitude towards using the Internet as a source of information.

H2: Easy access to the Internet is negatively associated with the student's knowledge retention.

H3. Students "college year" is positively associated with the student's knowledge retention.

H4: Perceived benefit of the borrowed information is positively associated with the student's demonstrability of knowledge

H5: Instructor's readiness "is positively associated with the student's knowledge retention

To bring an understanding of the complex issue of knowledge retention when college students obtain information from the internet, and to extend experience and add strength to what is already known through previous research, and to help predict the likelihood of knowledge retention, a survey was conducted. The participants were master degree graduate students and undergraduate students from several universities. A statistical analysis was performed on the collected data.

Data Collection and Analysis:

The questionnaires were analyzed to test the hypotheses using descriptive statistics, based on the descriptive nature of the information gathered. The statistical analysis method used is factor analysis. Tables were generated for the intuition of these descriptive statistics. To understand the constructs of the instrument well enough, an in depth analysis was undertaken to develop the scales and establish construct validity. Four rounds of sorting were used by four different groups of trained judges. The Inter-rater reliability scores were calculated for this stage with Cohen's Kappa scores, averaging 0.72. "For Kappa, no general authority exists with respect to required scores, but recent studies have considered scores greater than 0.65 to be acceptable" (Moore and Benbasat, 1991). Table 1 presents the research variables measured in the study, and table 2 presents the alpha coefficient of the instrument.

Table 1: Research Variables Measured in This Study

Variable #

Variable Construct

Type of construct

1

Perceived Relative Advantage of the Internet

Interaction

2

Perceived Compatibility of the Internet

Interaction

3

Perceived Self Efficiency

predictive capabilities

4

Perceived Demonstrability of Knowledge

predictive capabilities

5

Perceived Benefit of the Borrowed Information

predictive capabilities

6

Perceived Usefulness of knowledge

predictive capabilities

7

Perceived Instructors Readiness

predictive capabilities

8

Student's Age and Level of Education

predictive capabilities

Table 2: Alpha Coefficient of the Instrument

Construct

Items

Alpha

Perceived Relative Advantage of the Internet

5

0.87

Perceived Easy Access to the Internet

3

0.83

Perceived Self Efficiency

4

0.81

Perceived Results Demonstrability of Knowledge

4

0.78

Perceived Benefit of the Borrowed Information

2

0.79

Perceived Usefulness of knowledge

2

0.80

Perceived Instructors Readiness

3

0.82

Student's Age and Level of Education

2

0.71

Total number of items

25

In multiple regression results, a value R of 1 indicates that the dependent variable can be perfectly predicted from the independent variables. A value close to 0 indicates that the independent variables are not linearly related to the dependent variable. The R2 value explains the observed variability of adoption. Thus, R and R2 were calculated. The calculations supported the notion that the dependent variable can be perfectly predicted from the independent variables (see Table 3).

A total of 153 responses were received and used for this study. Of 153 students, 65.2% of them were undergraduate and 35% are graduate (see table 4). The majority of the participants were between the ages of 19 and 23 (108 participants), and 68% of the students were female students (104 students).

Table 3: Internal Consistency Reliability for Constructs at 95% coefficient Level and P <0.0001

Construct name

Item

Inter-item correlation

 

R

R2

Relative

Q8

1

 

 

 

 

0.873

0.762

Advantage

Q9

0.849

1

of the Internet

Q10

0.851

0.919

1

Q11

0.896

0.911

0.880

1

 

Q12

0.911

0.866

0.866

0.921

1

Easy Access to

Q13

1

 

 

 

 

0.620

0.384

the Internet

Q14

0.598

1

Q15

0.647

0.832

1

Self Efficiency

Q16

1

 

 

 

 

0.809

0.654

Q17

0. 827

1

Q18

0.771

0.823

1

Q19

0.855

0.870

0.862

1

Demonstrability of

Q20

1

 

 

 

 

0.846

0.716

Knowledge

Q21

0.891

1

Q22

0.893

0.863

1

Q23

0.771

0.753

0.781

1

Benefit of the

Q24

1

 

 

 

 

0.758

0.575

Borrowed Information

Q25

0. 758

1

Usefulness of

Q26

1

 

 

 

 

0.868

0.753

Knowledge

Q27

0.868

1

Instructors Readiness

Q28

1

 

 

 

 

0.648

0.420

Q29

0. 718

1

Q30

0.589

0.549

1

Student's Age and

Q31

1

 

 

 

 

0. 823

0.677

Level of Education

Q32

0.823

1

 

 

 

 

 

Table 4: Respondent distribution by School Year

Student Level

Frequency

Percentage

Freshman

22

14.38

Sophomore

19

12.42

Junior

31

20.26

Senior

36

23.53

Master

41

26.8

Ph.D. Students

4

2.61

Total

153

100

Hypothesis testing

The First Hypothesis (H1): : the students attitude towards using the Internet as a source of information, the mean attitude score is 4.08 in the scale of 1 to 7 where "1" stands for "extremely disagree" and "7" stands for "extremely agree". The standard deviation of the attitude score is 1.15. The median of the attitude score is 4.00, virtually the same as the mean. The distribution is fairly symmetric about the mean (see figure 1). The first and third quartiles are 3.25 and 5.00, respectively.

Figure 1: Students' attitude score

Null hypothesis: The average attitude score is equal to 3.5.

Alternative hypothesis: The average attitude score is greater than 3.5.

Using a one-sample t-test, we find an extremely small P-value, less than 0.1% (t-value is 6.66), which means that we can reject the null hypothesis at 0.1%, a very significant level. The number of observations is 153 (see Table 5). Based on the above testing result, we can conclude that students do not have a negative attitude towards using the Internet as a source of information.

Table 5: Statistics of attitude score

Number of Observations

First quartile

Third quartile

Median

Mean

St.Dev.

153

3.25

5

4

4.08

1.15

The Second Hypothesis (H2): The easy access to the Internet is negatively associated with the student's knowledge retention have been recorded such that "1" stands for "extremely agree" and "7" stands for "extremely disagree". In this way a higher score in this case represents higher level of negative Internet impact. The descriptive statistics of scores for students' tendency to retain knowledge and the perceived level of ease of access to the Internet are summarized in Table 6. The mean score for tendency to retain knowledge is 5.26 with a standard deviation of 1.19, in a scale of 1 to 7, and the mean score for the perceived level of ease of access to the Internet is 4.18 with a standard deviation of 0.60. The Pearson correlation coefficient between the students' tendency to retain knowledge and the perceived level of ease of access to the Internet is found to be 0.07, which indicates a very weak positive correlation. The P-value in testing the zero correlation is calculated as 0.34, which means the correlation is not statistically significant. Figure 2 illustrates Pearson correlation between the students' tendency to retain knowledge and the perceived level of ease of access to the Internet

Table 6: Statistics of scores for student's tendency to retain knowledge and the ease of access to the Internet

Observation number

First quartile

Third quartile

Median

Mean

St.Dev

Internet ease of access

153

3.50

5.00

4.50

4.18

1.19

Knowledge Retention

153

4.80

5.50

5.20

5.26

0.60

Figure 2: Pearson correlation between students' tendency to retain knowledge and the Internet ease of access

The Third Hypothesis (H3): Students college year (level of education) is positively associated with the student's knowledge retention. The descriptive statistics of Students college year is in Table 7. The mean score for the banks' assurances is 4.68 with a standard deviation of 0.71, in a scale of 1 to 7. then the Pearson correlation coefficient between the two measurements will be 0.16 and the corresponding P-value will be 0.04 or 4%. This means the positive correlation between the two measurements is statistically significant (at 5% significant level) though it is not strong. Figure 3 demonstrates the linear regression plot of the students' college year and students' knowledge retention.

Figure 3: Students' college year and students' knowledge retention

Table 7: Statistics of students' college year (level of education)

Number of Observations

First quartile

Third quartile

Median

Mean

St.Dev.

153

4.13

5

4.75

4.68

0.71

The Fourth Hypothesis (H4): The descriptive statistics of the perceived benefit of the information is positively associated with the student's demonstrability of knowledge are summarized in Table 8, and illustrated in figurer 4. To provide more intuitive demonstrations of the score distributions. The mean score for the perceived benefit of the information is 8.05 with a standard deviation of 2.88, in a scale of 3 to 14. And the mean score for the student's demonstrability of knowledge is 4.36 with a standard deviation of 0.96, in a scale of 1 to 7.

Table 8: The perceived benefit of the information and the student's demonstrability of knowledge

Number of Observations

First quartile

Third quartile

Median

Mean

St.Dev.

Perceived benefit of the information

153

6

8

10

8.05

2.88

Demonstrability of knowledge

153

3.7

4.4

5

4.36

0.96

Figure 4: Benefit of the information and student's demonstrability of knowledge

The Fifth Hypothesis (H5): Our last hypothesis is Instructor's readiness is positively associated with the student's knowledge retention. The descriptive statistics of Instructor's readiness is summarized in Table 9. The Pearson correlation coefficient between the Instructor's readiness and the student's knowledge retention is 0.66, which indicates a fairly strong positive correlation. The P-value in testing the zero correlation is less than 0.1%, which means the correlation is very significant. Figure 5 presents the linear regression plot of the Pearson correlation coefficient.

Table 9: Instructor's readiness

Number of Observations

First quartile

Third quartile

Median

Mean

St.Dev.

Technical Competence

153

28

42.5

35

35.98

9.46

Figure 5: Instructor's readiness and the student's knowledge retention

Conclusion and Recommendations

This study reveals the variables that influence knowledge retention and exhibit predictive capabilities. Although limited by a small participant sample (of four universities), the following conclusions appear justified. Overall, knowledge retained by college students, when information is obtained from the Internet, and despite the high level of students' usage of Internet as a source of information (92.6% of participants), the analysis showed that the Internet has low to moderate knowledge retention (72.9% of participants). This should flag an alert for educators when they rely on the Internet for their students' knowledge acquisition and retention. However, the results were better for the graduate students than they were for the undergraduate students. Within the undergraduate students, as determined by response outcomes, appeared to be influenced by numerous variables including age, gender (it is higher for female students), college year, computer skills, and time devoted to the topic. Additionally, these five variables exhibited predictive capabilities for knowledge retention. Self-Efficacy and the perceived usefulness of knowledge were the most important variables within the graduate educational environment that exhibited a relationship to the predictive capability for knowledge retention. Instructor's characteristic (Teaching Quality, Training and Experience, Readiness, and determination to have students discuss submitted work through presentation) was the second important factor.

This finding suggests that when students are asked to create a report (or do school work), where the Internet becomes a potential source of information, the instructor should draw out the students' existing knowledge through tasks and assignments that can reveal students' thinking. Also, the instructors need to create the assignment in a form that should make students further understand the subject matter. In addition, the instructors need to use frequent influential and significant assessments to make students' understandings apparent to their follow students, and instructors. These assessments, as the study can show, are very useful in promoting knowledge retention and enhance students' ability to repeat facts or demonstrate skills.

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