*Factors Influencing Information Literacy of University Students DOI: http://dx.doi.org/10.5772/intechopen.109436*

#### **Figure 2.**

*ILT score means according to demographic parameters (M—male, F—female; sci—natural sciences, non-sci social sciences; N = 561).*

between natural science and social science majors could mean that both groups had courses in their curricula that facilitated the development of IL. Pearson's correlation between study year and IL was 0.14, which is statistically significant but small. The negligible improvement in IL between academic years 1 and 2 and the dominance of general courses in year 1 suggest that more IL needs to be introduced in the second year, whether through a special IL course or through the existing courses.

#### **4.2 IL content**

RQ2: In which content areas of IL are students successful, and in which areas should they be given more emphasis in their education?

Partial ILT scores based on content categories were investigated (**Figure 3**). The lowest mean was achieved in the content category of legal and ethical use of information (A5—55%), followed by information search (A2—65%) and information need identification (A1—69%). Students were more successful in information use (A4— 73%) but especially in information evaluation (A3—83%). These results suggest that during IL courses, more emphasis should be put on ethical, legal, and socio-economic aspects of information use, as well as on advanced database searching techniques.

Lack of information searching skills can hinder students' research work, on one hand, as well as affects citizens' ability to verify information when confronted with dubious claims either in social media or in other information sources that may seem legitimate at first glance. On the other hand, the high level of competence in evaluating information may indicate that university students are not as susceptible to deliberate misinformation as the general population and that students are relatively good at applying criteria to evaluate the credibility of information sources.

Achievements in different IL content categories were interconnected with Pearson's correlations among categories ranging from 0.21 to 0.39. The lowest value was achieved between information use and the two weakest IL categories, namely information search and ethical issues.

When proficiency in individual IL content categories was studied in light of demographics, it turned out that female students performed significantly better in information evaluation than males, while social science majors performed better in ethical issues than natural science majors. The biggest difference among lower and higher year students was achieved in ethical issues (8% difference) and information use (10% difference), but students were closer in information evaluation (3% difference) and information search (5%).

#### **4.3 IL and scientific literacy**

RQ3: Is there a relationship between students' IL and their scientific literacy? Are students' abilities to master higher levels of cognition (understanding and applying knowledge) comparable between the two literacies?

On the SLT knowledge test, students achieved very similar total proficiency levels to the ILT test (mean 67.63% on ILT vs. 67.02% on SLT). Score distribution was similar as well. The two scores correlated significantly, with Pearson's correlation of 0.44.

IL and SL scores were evaluated on a cognitive subscale. Results showed a similar level of IL on the cognitive level of remembering (B1) and understanding (B2), but students were less successful in knowledge application (B3) (**Figure 4**). Their SL proficiency decreased more with each cognitive level.

In terms of demographics, no differences were observed between genders in IL or SL. As expected, natural science students were significantly better in SL than social science students, (understanding—B2; 72% vs. 66%, and applying—B3; 48% vs.

#### **Figure 4.**

*Comparison of ILT and SLT score means according to cognitive categories (whiskers represent SD; N = 561).*

*Factors Influencing Information Literacy of University Students DOI: http://dx.doi.org/10.5772/intechopen.109436*


**Table 5.**

*Pearson's correlation of ICT scales with ILT score (N = 561).*

43%). Differences among study years 2 and 3–4 were observed mostly in the lowest two cognitive categories, but not in knowledge application.

#### **4.4 IL and ICT**

RQ4: Does software use, ownership of ICT devices, number of ICT-rich courses, and confidence using the Internet affect students' level of IL?

Results showed no correlation between ILT score and device ownership (ICT-H, **Table 5**), nor between ILT and number of ICT-rich study courses (ICT-C). Correlation with software use (ICT-S) was slightly higher, but the highest and statistically significant correlation was with confidence using the internet (ICT-I), reaching 0.19.

ICT parameters were interrelated: software use correlated with hardware possession and internet confidence (r = 0.38), while hardware possession also correlated with internet confidence (r = 0.19).

Analysis by demographic parameters showed that male students used software more often, possessed more devices, and were more confident using the internet than females, despite female students taking more ICT-rich courses. Social sciences majors used software more often, possessed more devices, and were more confident using the internet than natural sciences majors. Significant increase in software use and internet confidence was observed from year 2 to years 3–4.

#### **4.5 IL and psychological leanings**

RQ5: How is IL influenced by various psychological/learning parameters, such as self-concept about learning and problem-solving, general self-efficacy, use of metacognitive learning strategies, internal motivation, and autonomous and controlled external motivation?

Three of the seven psychological scales correlated significantly with the ILT score (**Table 6**): self-concept about learning (SC-L) and problem-solving (SC-P) as well as self-efficacy (SE). This result was expected as the abilities to learn, solve problems, and be efficient are more likely to lead to success. With low correlation, the use of metacognitive learning strategies (LS) was not found as an important factor. When students did not understand the material, they asked their classmates rather than a teacher for help.

Motivation played a smaller role (**Table 6**). Internal motivation (IM) and autonomous external motivation (EM-A) correlated only slightly with the ILT score. Internally motivated students highly rated their interest in and understanding of the field of study. In the external autonomous scale, students most acknowledged


**Table 6.**

*Pearson's correlation of psychological/learning scales with ILT score (N = 561).*

the value of learning for their future—their employment prospects and professional development. Controlled external motivation (EM-C) did not correlate well with ILT. Item analysis of this scale showed that most students did not consider it important to make an impression on the teacher, parents, or their peers but that they nevertheless relied on the teacher's authority and were motivated by good grades.

All psychological parameters correlated rather heavily among themselves. For example, the strongest link was found between self-concept about learning and internal motivation, and another link between self-concept about problem-solving and self-efficacy (both *r* = 0.67). In other correlations related to IL, three of the psychological parameters correlated significantly to student confidence using the internet (scale ICT-I), namely self-concept about problem-solving (*r* = 0.29), self-efficacy (*r* = 0.31), and internal motivation (*r* = 33).

Regarding demographic parameters, female students scored significantly higher in self-concept about learning and using metacognitive learning strategies, while male students scored higher in self-concept about problem-solving. The type of study major played no role in the psychological parameters, but year of study did in all, except in problem-solving. The problem-solving ability seems to be a personal characteristic rather than an acquired skill.

#### **4.6 IL study course and teaching methods**

RQ6: To what extent does a study course with IL content contribute to improving students' information literacy? How do the teaching methods affect the outcomes?

Results are shown for the subgroup of 151 students who were enrolled in an information literacy course and took the ILT test before (pre-test) and after the course (post-test). Students' mean IL level improved significantly from 65% on the pre-test to 80% on the post-test (**Figure 5**). Significant improvement was achieved in all IL content categories, but it was the highest in information use (A4—25%), information search (A2—19%), and ethical issues (A5—17%). The lowest increase was observed in information evaluation (A3—10%) due to the fact that the pre-test level was already high. When it came to cognitive categories, the largest increase was obtained in the highest category of applying (B3—22%) and the lowest in understanding (B2—11%).

#### **Figure 5.**

*Comparison of ILT scores on the pre-test and post-test for total IL, five content and three cognitive categories (N = 151).*

*Factors Influencing Information Literacy of University Students DOI: http://dx.doi.org/10.5772/intechopen.109436*

**Figure 6.** *ILT score means for three teaching methods (NLEC = 52, NPRJ = 52, NPBL = 47) on the pre-test and post-test.*

Students were divided into three groups, based on the teaching method applied in the course: traditional lectures (52 students), project-based learning (52 students), and problem-based learning (47 students). Improvement in total IL according to the teaching method is shown in **Figure 6**. The biggest improvement was achieved in project-based learning group (PRJ—18%), followed by the problem-based group (PBL—16%). The traditional lecture group (LEC) improved for 11%, suggesting that the active teaching methods were more successful and could be recommended for university IL study courses.

**Figure 7** shows the pre-test scores and progress achieved on the post-test for each of the three teaching methods in every IL content categories. The biggest improvement for all three teaching methods was achieved in the IL category of information use (A4; 15–42%), followed by information search (A2; 13–26%) and legal/ethical issues (A5; 13–18%). The lowest progress was obtained in the category of information evaluation (A3; 5–13%) and identification of information needs (A1; 10–15%). Both groups using active teaching methods (project- and problem-based learning)

#### **Figure 7.**

*ILT scores on the pre-test and the progress achieved on the post-test for three teaching methods (NLEC = 52, NPRJ = 52, NPBL = 47) and IL content categories.*

#### **Figure 8.**

*ILT scores on the pre-test and the progress achieved on the post-test for teaching methods (NLEC = 52, NPRJ = 52, NPBL = 47) and IL cognitive categories.*

achieved similar post-test proficiency in all five content categories, which was above that of the traditional lecture group.

Another look at improvements across the cognitive categories (**Figure 8**) shows the highest increase for all three teaching methods in the highest category of applying knowledge (B3; 19–27%), followed by the lowest category of remembering (B1; 10–17%), while improvement was the lowest in the category of understanding (B2; 6–15%). The two active learning methods outperformed the lecture-based approach in most cases.
