Posters were prepared by the students of Nagoya Institute of Technology. In July 2016, a “recital” was held where students presented their individual learning programs and their career goals after graduation―this was called the “C-plan.”¹ Each student prepared a poster the size of two A3 (11.7 × 16.5 inches) pages, which the present study employs as materials to which text mining was applied. Compared with the usual kinds of documents used in this approach, the amount of information in the posters might be a little limited, but it is nevertheless likely that the posters are sprinkled with important keywords worth picking out, because the students will likely have delicately considered and chosen the words they used due to space constraints. The text mining tool KH Coder was used for the analysis.

The authors of the posters were university freshmen enrolled in the Creative Engineering Education Program in the university’s faculty of engineering. All students belonged to one or the other of the following two courses depending on their choice at their entrance examination: “Materials and Energy” (ME hereafter; 62 students) and “Computer and Social Engineering” (CS; 42 students) course. Specific topic areas covered by each course are listed in Table 1.

Posters prepared by 104 students, pooled between the two courses, were employed for the analysis. The number of words extracted from the posters was

17,983 in total, 2975 of which were unique. The 100 most frequently appearing words are summarized in Table 2. As seen in the table, overwhelmingly common words include “development” and “technology,” which seems natural in that the authors of the posters are students in the faculty of engineering. Some words such as “goal,” “study,” and “learn” would be used in a general sense to construct a learning program. It is noteworthy that even though the students were university freshmen only three months into their program, some of them had a good command of technical engineering terms such as “live body,” “sugar chain,” “catalyzer,” “macromolecule,” and “synthesis.”

To distinguish between general words on posters and specific words giving information on students’ learning programs and career goals and to examine how frequently given words were used by the students, a hierarchical cluster analysis was conducted it identified words that appeared in the posters at least 17 times and grouped them into five clusters, as shown in Table 3. The five clusters can be characterized as follows.

Cluster1 consists of the following five words: “challenge,” “present situation,” “change,” “value,” and “realization.” These words suggest that the students are highly motivated to create something new and valuable.

Cluster 2 consists of the following four words: “goal,” “career,” “study,” and “plan.” These words are commonly used among the students to construct posters.

Cluster 3 is characterized by the following typical words: “technology,” “development,” “universe,” “research,” “disaster,” and “earthquake.” These words suggest that the students are willing to work in research and development to deal with future risk or unknown territory. In particular, after the Great East Japan Earthquake in March 2011, they would have become more aware of disaster- prevention measures and the role of engineering therein.

Cluster 4 is characterized by the following typical words: “efficiency,” “method,” “power generation,” “nature,” “healthcare,” “cost,” “light,” “live body,” and “use.” These words suggest that the students are interested in improving existing technologies or saving energy and resources, in addition to creating whole new technologies/structures/concepts.

Cluster 5 is characterized by the following typical words: “solution,” “problem,” “design,” “application,” “think,” “knowledge,” “necessary,” and “possibility.” These words suggest that the students value knowledge and problem-solving thought to overcome present issues.

For the analysis of differences between the two courses, their data were separated. Taking into consideration the frequently used words previously found, the following three coding rules were produced to formulate groups of words used in a similar context.

Ÿ Human beings: “disaster,” “earthquake,” “human beings,” “robots,”“people.”

Ÿ Technology: “development,” “technology,” “research,” “efficiency,”“cost.”

Ÿ Value creation: “challenge,” “present situation,” “change,” “value,” “realization.”

For example, according to the first coding rule, if a sentence in a poster contains at least one word such as “disaster,” “earthquake,” or “human beings,” the code “human beings” is given to the sentence.

Table 4 is a cross-tabulation table that compares the appearance ratios of codes under the two courses. As we can see from the table, the appearance ratio of the code “human beings” under the ME course was lower than that under CS. In contrast, the code “technology” was under ME than under CS. These results were statistically supported by chi-squared tests; both differences were significant at the 1% level. Overall, it appeared that CS courses are more human oriented while ME courses are more technology oriented, which is natural given the content of the courses. Finally, the appearance ratio of “value creation” was not significantly different across the two courses at the 10% level; that is, students in both courses used words related to value creation with approximately the same frequency.