Creative thinking methods to improve college students’ ability to solve network problems
（A creative thinking approach to enhancing the web-based problem
solving performance of university students）
Computers & Education 72 (2014) 220–230
1. Concept analysis
1. Network problem solving ability
Internet problem-solving ability refers to a person's ability to retrieve information on the Internet to answer a series of questions related to a target problem. With the emergence of the Internet and communication technology (ICT), schools not only play the role of imparting knowledge to students, but also cultivate students' ability to solve problems on the Internet to quickly respond to the challenges of a diversified society. The essential. It is necessary to promote students' problem-solving abilities through training courses on information retrieval, identification and reorganization processes to adapt to the changing society in the future.
2. Creative problem-solving strategies
Creative thinking isgoing beyond learned principlesandcreating new ways to solve problems process. The researchers noted that these two processes can be integrated into a single complex process called creative problem solving. Figure 1 shows the creative problem solving model. It consists of three parts:
Figure 1, Three components and six stages of creative problem solving
(1) Understand the problem. This component consists of three phases: Mess Discovery (MF), which refers to identifying and selecting a goal, job, or problem; Data Discovery (DF), which refers to collecting data and exploring the first phase Factual feelings, impressions, observations, questions or work, and determining the focus; Problem Discovery (PF), that is, discovering problems and confirming them; Narrating and refining the problem to make it clear.
(2) Generate ideas. This component is related to the fourth stage: Idea Search (IF), which refers to doing your best to find many diverse and novel ideas, options and alternatives.
(3) Action plan. This part consists of two phases: Solution Finding (SF), which refers to developing a set of evaluation criteria and using these criteria to evaluate and clarify the merits of all ideas discovered in the previous phase; Weaknesses and appropriateness, and choosing the most useful solution; Acceptance Finding (AF), which refers to the use of the chosen solution, considering potential resistance and help during implementation, and enabling the solution to develop effectively and achieving a specific action plan.
3.Cognitive style
Cognitive style refers to the way an individual thinks, perceives, and processes information to solve problems through interaction with the environment. Researchers have proposed different cognitive styles from different perspectives. People with different cognitive styles often exhibit very different behaviors when perceiving and processing information.
2. Research content
1. Research background:
With the advancement of information and communication technology, researchers have pointed out the necessity and challenges of developing effective teaching strategies to improve students’ network problem-solving abilities.Network problem-solving abilities Refers to the ability to investigate a range of related questions by searching, abstracting, and summarizing information on the Internet. Therefore, this study proposes a creative thinking method that integrates creative problem-solving strategies into an online learning environment and conducts experiments in an "Information Technology and Society" course at a university.
2. Research purpose:
This study proposes acreative thinking strategy to deal with this problem. An experiment was conducted on 80 freshmen from two classes of a university to evaluate the effectiveness of the proposed method.
3. Research objects:
Two classes in the University Nursing DepartmentThere are 80 freshmen, 23 males and 57 females, aged 19 to 20 years old. All students were taught by the same teacher and had experience using computers and accessing the Internet prior to the learning activities. One class was designated as the experimental group and the other class was designated as the control group. 40 students in the experimental groupadopted creative thinking methods to conduct network-based problem-solving activities, 40 students in the control group< /span>The same learning activities are carried out using traditional teaching methods.
4. Research questions:
To delve deeper into the effectiveness of this approach, students’ cognitive styles were taken into account when analyzing their learning performance. The research questions of this study are as follows:
- Do students who use creative thinking methods have better online problem-solving skillsthan students who use traditional teaching methods?
- Do students with different cognitive stylesshow different online problem-solving abilities in creative thinking teaching and traditional teaching?
5. Research results:
Experimental results show thatthis method is effective in"problem discovery" and "idea discovery" Compared with traditional methods, students' network problem-solving abilities are improved.
In addition, this study also found that this method can improve the "fact-finding" performance of students withthe intuitive cognitive style.
3. Experimental design
The effectiveness of this method was evaluated using the "Information Technology and Society" course at a university in southern Taiwan. One of the goals of the elective course is to help students understand the problems of the living environment and society and develop their ability to use information technology to solve problems.
1. Experimental subjectsand method:Two classes of the nursing department of a university80 freshmen , 23 males and 57 females, aged 19 to 20 years old.
All students are taught by the same teacher and have experience using computers and accessing the Internet prior to learning activities. One class was designated as the experimental group and the other class was designated as the control group. 40 students in the experimental groupadopted creative thinking methods to conduct network-based problem-solving activities, 40 students in the control group< /span>Use traditional teaching methods to conduct the same learning activities.
2. Experimental tools:Research using the network-based learning system Meta-Analyzer
Meta-Analyzer allows teachers to design a series of progressive questions to guide students to build topic knowledge based on specific topics in the learning course. The interface of Meta-Analyzer is shown in Figure 2.
Figure 2. A student searches for information and answers questions in Meta-Analyzer’s student interface.
3. Experimental process:The experimental process is shown in Figure 3
Figure 3. Experimental steps of this study
4. Result:
Pre- and post-test raw data
Pretest t-test:
There was no significant difference in the pre-test scores of the two groups of students through the t test (t = 0.382, p>0.05), indicating that the network problem-solving abilities of the two groups of students before the learning activities were basically equivalent.
Posttest analysis of covariance:
Table 1: Covariance analysis of network problem-solving abilities of the two groups
| Group |
N |
Mean |
SD |
df |
Adjusted average |
F |
h2 |
|
| Web-based problem solving skills |
control group |
40 |
22.48 |
7.06 |
77 |
22.20 |
4.267 |
0.09 |
| test group |
40 |
26.85 |
6.90 |
26.90 |
In order to evaluate the effectiveness of the proposed method, analysis of covariance (ANCOVA) was used to examine the post-test scores of the two groups and eliminate the influence of the pre-test scores. Table 1 shows that the academic performance of students in the experimental group is significantly better than that of students in the control group (F(1,78)=4.267, p <0.007), Effect size(η2) is greater than 0.090, showing a relatively Large effect size. This shows that creative problem-solving methods can improve students' online problem-solving abilities.
(Supplement: eta squared) is the commonly used effect size in analysis of variance. Its calculation method eta2=SS between groups/SS overall
η2=0.0099-0.0588 small effect, 0.0588-0.1509 medium effect, greater than 0.1509 large effect)
Students’ post-test scores on various dimensions of online problem solvingOriginal data:
T-test results of students’ post-test scores on various dimensions of online problem solving:
Problem Discovery Covariance Analysis:
Idea to find covariance analysis:
Table 2 Post-test of problem-solving process using independent sample t test
| problem solving process indicators |
Group |
N |
df |
Mean |
SD |
t |
Partial eta square |
| fact finding |
control group |
40 |
79 |
6.63 |
1.25 |
-0.778 |
0.008 |
| test group |
40 |
6.83 |
1.03 |
||||
| Problem discovery |
control group |
40 |
79 |
4.50 |
1.18 |
-7.464 |
0.417 |
| test group |
40 |
7.50 |
1.39 |
||||
| idea discovery |
control group |
40 |
79 |
2.80 |
1.11 |
-7.303 |
0.406 |
| test group |
40 |
5.30 |
1.86 |
||||
| Solution finding |
control group |
40 |
79 |
6.13 |
1.36 |
-2.387 |
0.068 |
| test group |
40 |
6.80 |
1.16 |
Table 2 further shows the t-test results of students’ post-test scores on various dimensions of online problem solving. The results showed that the two groups of students showed significant differences in the two dimensions of problem discovery (t=-7.464, p < 0.05) and idea discovery (t=-7.303, p < 0.001).The effect size (h2) is greater than 0.14 and shows a large effect size. In other words, creative problem-solving methods help improve students' ability to identify problems and discover ideas
Table 3 classifies the cognitive styles of the two groups of people
| cognitive style |
Group |
Fraction |
Number of students |
| Analytical |
control group |
≥55 |
13 |
| test group |
≥55 |
11 |
|
| Intuitive |
control group |
≤45 |
11 |
| test group |
≤45 |
11 |
The bottom 27% of students with CSI scores are classified as intuitive, while the top 27% of students with CSI scores are classified as analytical (Spanier & Tate, 1988). Table 5 shows the number of intuitive and analytical students in both groups.
T-test results of the pretest scores of two groups of students with the same cognitive style. The difference in pre-test scores between the two groups of students with the same cognitive style is not statistically significant. The difference in pre-test scores among analytical students is 0.22 (p < 0.05 ), the difference in pretest scores among intuitive students was 0.15 (p < 0.05). That is, both groups of intuitive and analytical students had comparable web-based problem-solving skills before the learning activity.
Posttest covariance analysis of analytical network problem-solving ability:
Posttest covariance analysis of intuitive network problem-solving ability:
Table 3 Post-test independent sample t-test of network problem-solving ability under different cognitive styles
| cognitive style type |
Group |
N |
df |
Mean |
SD |
t |
Partial eta square |
| Analytical |
control group |
13 |
23 |
21.31 |
1.11 |
3.42 |
0.347 |
| test group |
11 |
24.55 |
3.21 |
||||
| Intuitive |
control group |
11 |
21 |
21.27 |
1.19 |
4.40 |
0.492 |
| test group |
11 |
28.27 |
5.14 |
P<0.05
Table 3 shows the t-test results of the post-test scores of the two groups of students with the same cognitive style. The results showed that the post-test scores of intuitive students in the experimental group were significantly higher than those in the control group (t = 4.40 < /span>), the effect size (h2) is greater than 0.14, and has a large effect size , suggesting that creative problem solving helps intuitive students more than analytical students. 2, p < 0.0
Creative problem-solving strategies on analytical students’ online problem-solving performanceOriginal data:
Table 4 Multivariate analysis of variance of analytical learning problem-solving process indicators
| problem solving process indicators |
Control group (n=13) |
Experimental group (n=11) |
F |
Partial eta square |
|||
| df |
Means |
SD |
Means |
SD |
|||
| fact finding |
1 |
6.46 |
1.51 |
6.27 |
1.35 |
0.103 |
0.005 |
| Problem discovery |
1 |
5.85 |
1.14 |
6.45 |
1.57 |
1.200 |
0.052 |
| creative discovery |
1 |
3.77 |
2.09 |
6.00 |
2.00 |
7.067 |
0.243 |
| Find a solution |
1 |
5.54 |
2.33 |
6.82 |
0.98 |
2.867 |
0.115 |
P<0.005
Table 4 shows the results of multivariate analysis of variance excluding the effect of pretest. The results showed that the two groups of analytical students reached a significant level (F(1, 21)=7.067, p < 0.01), the effect size (h2) exceeds 0.14, showing a large effect size. That is, creative problem solving significantly benefits in improving the idea discovery performance of analytical students.
Creative problem-solving strategies onIntuitiveStudents’ online problem-solving performanceOriginal data: a>
Multivariate analysis of variance on the problem-solving process indicators of intuitive-type students:
Table 5 Multivariate analysis of variance of indicators of intuitive learning problem-solving process
| problem solving process indicators |
Control group (n=11) |
Experimental group (n=11) |
F |
Partial eta square |
|||
| df |
Means |
SD |
Means |
SD |
|||
| fact finding |
1 |
6.91 |
1.13 |
7.82 |
0.60 |
5.495 |
0.216 |
| Problem discovery |
1 |
5.73 |
1.01 |
7.36 |
1.75 |
7.232 |
0.266 |
| creative discovery |
1 |
3.45 |
1.81 |
6.00 |
2.00 |
9.800 |
0.329 |
| Find a solution |
1 |
6.55 |
4.68 |
7.27 |
1.79 |
0.232 |
0.011 |
P<0.05
Table 5 shows the results excluding the influence of pretest covariance analysis. The results showed that the intuitive students in the experimental group had better performance in “fact discovery” (F(1,20)=5.495, p < 0.05), “problem discovery” (F(1,20)=7.232, p < 0.05) and “ The performance in "idea discovery" (F(1,20)=9.800, p < 0.05) was significantly better than that of the control group, the effect size (h2) was greater than 0.14, and showed a large difference.
5. Conclusion
In this study, a creative problem-solving method is proposed to improve students' online problem-solving abilities.
And the learning performance of students with different cognitive types was evaluated through experiments. The results showed that creative problem solving was more helpful for intuitive students than for analytical students. In addition, further analysis results show that the proposed method can significantly improve the idea-seeking performance of analytical students. At the same time, the performance of "fact discovery", "problem discovery" and "idea discovery" of intuitive students is improved. Some literature points out that intuitive people tend to make quick judgments based on their feelings and like to explore the environment when solving problems. This may be why creative problem-solving methods have a greater impact on intuitive students than analytical students.
Therefore, this study provides potential strategies for cultivating students' thinking skills (such as search ability, creative thinking ability, critical thinking ability, and reasoning ability) and problem-solving abilities. That is, the developed web-based creative thinking learning environment can effectively help students come up with potential solutions with divergent and convergent thinking processes, rather than the traditional CPS environment. Additionally, in a web-based creative thinking environment, educators can have the opportunity to investigate changes in students' problem-solving processes before and after testing. At the same time, it also shows the effectiveness of this method in helping students with different cognitive styles solve personal network problems.
limitation:
①The sample is limited to one group, university nursing students.
②In addition, although this study focuses on cognitive style, it should be noted that other human factors may also affect learners' interaction with the Internet, including emotional factors, gender differences, and age differences.
Questions: 1. Why do two groups need to use analysis of variance? (can be used occasionally)
2. When it comes to the influence of intuitive and analytical learning problem-solving process indicators, why does one use multivariance and the other does not use multivariate variance to eliminate front-side interference?