At present, applying text mining techniques to educational data is attracting much research attention. The present study uses text mining techniques to examine posters prepared by university freshmen in engineering fields to present their learning programs and their career goals after graduation, under the expectation that important keywords worth identifying lurked in the posters. The results showed that even though the participating students were only three months into their university education, their learning programs and career goals were already rather concrete and well adapted to the fields and courses they had chosen. Some of them had a remarkably good command of technical engineering terms.
Over the last few decades, web-based learning has become more and more common and has been recognized as a potentially very effective educational method and resource. Web-based learning systems automatically collect and record a huge amount of data on students’ learning behavior as students use them. To exploit this goldmine of educational data and use it to understand better how students actually proceed with learning, data-mining techniques have begun to be applied to educational data. This active research field is called educational data mining (Romero & Ventura [
As a relatively recent development within this field, text mining techniques have been applied in educational research, allowing researchers to analyze text data such as formal text documents as well as informal ones like e-mails, chat messages, digital diaries, and online questions. Studies adopt a text mining approach to educational data include Hung [
In line with these pioneering works, the present study examines university engineering students’ posters describing their learning programs and career goals using text mining techniques. University freshmen prepared the posters to explain their individual learning programs and their career goals after graduation, and it can be expected that important keywords for our understanding of the students’ learning status and progress worth picking out lurk in the posters.
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
Posters prepared by 104 students, pooled between the two courses, were employed for the analysis. The number of words extracted from the posters was
Materials and energy (ME); 62 students | Computer and social engineering (CS); 42 students |
---|---|
Life and materials chemistry | Networks |
Soft materials | Computational intelligence |
Advanced ceramics | Multimedia and human computers |
Materials function and design | Architecture and design |
Applied physics | Civil and environmental engineering |
Electrical and electronic engineering | Systems management and engineering |
Mechanical engineering |
17,983 in total, 2975 of which were unique. The 100 most frequently appearing words are summarized in
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
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.
Word | No. | Word | No. | Word | No. | Word | No. |
---|---|---|---|---|---|---|---|
Development | 141 | Japan | 25 | Sugar chain | 16 | Action | 11 |
Technology | 126 | Necessary | 24 | Various | 16 | Instrument | 11 |
Goal | 84 | Solution | 23 | Utilization | 15 | Decrease | 11 |
Study | 79 | Light | 23 | Make | 15 | Reality | 11 |
Challenge | 58 | Power generation | 23 | Catalyzer | 15 | Contribution | 11 |
Realization | 54 | Efficiency | 21 | Art | 15 | Few | 11 |
Learn | 51 | Think | 21 | World | 15 | New | 11 |
Present state | 49 | Design | 21 | Battery | 15 | Load | 11 |
People | 49 | Earthquake | 20 | Frequent | 14 | Influence | 10 |
Robot | 46 | Cost | 19 | Many | 14 | Sound | 10 |
Career | 45 | Universe | 19 | Data | 13 | Home | 10 |
Relationship | 44 | Nature | 19 | Safety | 13 | Strong | 10 |
Research | 44 | Fuel | 19 | Subject | 13 | Macromolecule | 10 |
Value | 39 | Employ | 19 | Slope | 13 | Synthesis | 10 |
Life | 38 | Healthcare | 17 | Improvement | 13 | Accident | 10 |
Change | 34 | Conduct | 17 | Structure | 13 | Acquisition | 10 |
Application | 32 | Use | 17 | High | 13 | Treatment | 10 |
Plan | 31 | Usage | 17 | Brain | 13 | Production | 10 |
Present | 31 | Knowledge | 17 | Plastic | 12 | Product | 10 |
Disaster | 30 | Method | 17 | Science | 12 | Occurrence | 10 |
Automobile | 30 | Driving | 16 | Space | 12 | Disease | 10 |
Problem | 29 | Have | 16 | Now | 12 | Drug | 10 |
Possibility | 26 | Self-action | 16 | Especially | 12 | Panel | 9 |
Human beings | 26 | House | 16 | Heat | 12 | Movement | 9 |
Live body | 26 | New | 16 | Aspire | 12 | Medicinal product | 9 |
Cluster 1 | Cluster 3 | Cluster 4 | Cluster 5 |
---|---|---|---|
Challenge | Technology | Efficiency | Solution |
Present situation | Development | Method | Problem |
Change | Universe | Japan | Design |
Value | Research | Power generation | Employ |
Realization | Robot | Nature | Application |
Cluster 2 | People | Healthcare | Present |
Goal | Disaster | Cost | Think |
Career | Earthquake | Light | Knowledge |
Study | Human beings | Live body | Necessary |
Plan | Conduct | Learn | Automobile |
Use | Fuel | ||
Relationship | Usage | ||
Life | |||
Possibility |
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.
Human beings: “disaster,” “earthquake,” “human beings,” “robots,”“people.”
Technology: “development,” “technology,” “research,” “efficiency,”“cost.”
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.
The present study examined university students’ learning program posters using text mining techniques. It was found that even though the students were university freshmen and only three months had passed between the beginning of their university engineering education and their preparation of the posters, their learning programs and career goals were rather concrete and well adapted to
Human beings | Technology | Value creation | No. of observations | |
---|---|---|---|---|
ME course | 56 (5.80%) | 181 (18.76%) | 95 (9.84%) | 965 |
CS course | 56 (10.65%) | 67 (12.74%) | 46 (8.75%) | 526 |
Sum | 112 (7.51%) | 248 (16.63%) | 141 (9.46%) | 1491 |
Chi-squared | 10.808* | 8.465* | 0.361 |
Note: *denotes significance at the 1% level.
their fields and courses. This result suggests that the majority of the students had thought ahead about their future careers before admitted to university, instead of only after.
The results of the present study could be enriched by the following expansions. First, it might be interesting to apply text mining techniques to the learning programs of students majoring in fields other than engineering and compare the results to the current results. Second, it would be useful to trace how students’ career plans change as their education advances. These issues should be tackled by future research.
Kumakawa, T. (2017) A Text Mining Examination of Uni- versity Students’ Learning Program Posters. Open Access Library Journal, 4: e3639. https://doi.org/10.4236/oalib.1103639