This study investigates the undergraduate students in computer science/electric engineering (CS/EE) in Taiwan to measure their perceived benefits from the experiences in service learning coursework. In addition, the confidence of their professional disciplines and its correlation with service learning experiences are examined. The results show that students take positive attitudes toward service learning and their perceived benefits from service learning are correlated with their confidence in professional disciplines. Furthermore, this study designs the knowledge model by Bayesian network (BN) classifiers and term frequency-inverse document frequency (TFIDF) for counseling students on the optimal choice of service learning.
Educators in universities have recognized a fact that merely professional knowledge is not sufficient in real-world competition. In addition to professional know-how, the key success factors to a remarkable career include integrity, leadership, interpersonal skills, sympathy to others, and sense of social responsibilities. So students are motivated to provide voluntary services for the public and communities that can play a key role as connections to their social networks. Service learning is a form of experiential education in which students engage in activities that address human and community needs together with opportunities designed to promote student learning and development [
Service learning has a long history in the United States and has been recognized as key curriculum component in universities [
On the other hand, the challenges of service learning have been reported. Some people argue that it is neither the college’s place nor position to require service learning for students. Faculty also faces the challenge that the community should not be treated as a laboratory. Besides it is difficult to prove that service learning experiences are related to the students’ curriculum [
This study intends to aim at students’ perception of service learning experiences. Among the researches on measuring the perceived outcomes, most investigate the students in social science disciplines while the benefits for engineering students are to be explored. This study first investigates the undergraduate students in computer science/electric engineering (CS/EE) to measure their perceived benefits from service learning coursework. Besides, the benefits of service learning and their correlation between the professional disciplines are tested. Furthermore, this study advances one further step to construct the counseling knowledge base using Bayesian networks. We analyze students’ profiles and their feedback by term frequency-inverse document frequency and design the Bayesian network that provides advices to students for their optimal type of service learning.
Term frequency-inverse document frequency (TFIDF) [
Suppose term
The remainder of this paper is organized as follow. Section
In the first stage, this study aims at the benefits of service learning and their relationships between professional capabilities. The purposes of this stage are (1) exploring the benefits of service learning course by measuring the attitudes of students in CS/EE, (2) examining students’ confidence in the fields of CS/EE, (3) testing the correlation between the benefits of service learning and students’ professional confidence, and (4) examining the relation between the benefits/confidence and the perceived mutual effects between service learning and professional confidence. The research framework is shown as Figure
The framework for benefit measurement.
Based on the framework in Figure H1: students in CS/EE perceive considerable benefits from the service learning course. H2: students in CS/EE perceive considerable confidence from professional learning.
To test the correlation between the benefits of service learning and the confidence in professional discipline, the following hypotheses are tested. H3: students perceive that their capabilities and experiences gained from service learning assist in learning the professional discipline. H4: students perceive that their capabilities in the professional discipline assist in the service learning coursework.
Besides, we test the following hypotheses on the mediating effects between the perceived benefits of two types of learning and their correlation. H5: students who perceived high benefits from service learning are likely to perceive high benefits from professional discipline (and vice versa).
We survey the attitudes of undergraduate students in CS/EE at one leading national university in Eastern Taiwan. The samples are students registered in service learning as a requisite course for two semesters during 2010–2012. In each semester they are required to provide at least 18 hours voluntary services on or off campus, under supervision of the faculty. On completion of the coursework, the students are invited to fill the questionnaires composed of 20 questions on service learning, 9 questions on professional learning, 2 questions on service experiences, and a set of questions about personal information. The total sample size is 256, including 143 in electric engineering and 113 in computer science. Finally the effective sample size is 116, among which 59 (50.86%) in electric engineering and 57 (49.14%) in computer science.
The questionnaire is prepared based on [
The structure of the questionnaire.
Part 1: benefits from service learning | |
(1) Personal growth | |
(2) Ability to work with others | |
(3) Leadership skills | |
(4) Communications skills | |
(5) Understanding cultural and racial differences | |
(6) Social responsibility and citizenship skills | |
(7) Community involvement | |
(8) Applying knowledge to the “real world” | |
(9) Problem analysis and critical thinking | |
(10) Social self-confidence | |
(11) Conflict resolution | |
(12) Ability to assume personal responsibility | |
(13) Development of caring relationships | |
(14) Gaining the trust of others | |
(15) Sensitivity to the plight of others | |
(16) Workplace skills | |
(17) Ability to make a difference in the community | |
(18) Skills in learning from experience | |
(19) Organizational skills | |
(20) Connecting theory and practice | |
|
|
Part 2: confidence from professional learning | |
(1) Basis in CS/EE, mathematics, science, and engineering | |
(2) Design/implementation of experiments, analysis, and explanation of results | |
(3) Capability for using programming languages, application systems, and instruments of CS/EE | |
(4) Capability for hardware and software development | |
(5) Computer hardware design and computer networks | |
(6) Teamwork and communication skills | |
(7) Discovery, understanding, integration, and problem-solving in CS/EE | |
(8) Understanding the impacts of CS/EE on environments and continuous learning | |
(9) Understanding the ethical and social responsibilities of CS/EE | |
|
|
Part 3: correlation between service learning and professional discipline | |
(1) The capability and experience gained from service learning assist in learning the professional discipline. | |
(2) The capability and experience in the professional discipline assist in the service learning coursework. | |
|
|
Part 4: personal information | |
Please describe your experiences in service learning in words, for example, the remarkable interactions, how your experiences from service learning assisted professional learning, or the contributions of your professional background that might help in service learning | |
Gender | |
Year | |
Major | |
Organization served | |
Type(s) of services |
The results of Part 1 are processed by factor analysis and yield a four-factor solution. The rotated factor matrix is shown in Table
The rotated factor matrix of service learning benefits.
Item* | Factor | |||
---|---|---|---|---|
1 | 2 | 3 | 4 | |
(13) Development of caring relationships |
|
−0.208 | −0.030 | 0.153 |
(16) Workplace skills |
|
−0.018 | 0.178 | −0.191 |
(12) Ability to assume personal responsibility |
|
−0.050 | 0.214 | −0.128 |
(14) Gaining the trust of others |
|
0.383 | −0.027 | −0.100 |
(17) Ability to make a difference in the community |
|
0.238 | −0.374 | 0.471 |
(18) Skills in learning from experience |
|
0.141 | 0.127 | 0.172 |
(9) Problem analysis and critical thinking | −0.120 |
|
0.012 | 0.158 |
(8) Applying knowledge to the “real world” | −0.149 |
|
0.030 | 0.176 |
(2) Connecting theory and practice | 0.207 |
|
0.066 | 0.028 |
(11) Conflict resolution | 0.128 |
|
0.212 | −0.059 |
(10) Social self-confidence | 0.011 |
|
0.389 | −0.089 |
(19) Organizational skills | 0.322 |
|
0.229 | 0.007 |
(20) Ability to work with others | 0.143 | 0.024 |
|
0.030 |
(1) Personal growth | 0.102 | 0.060 |
|
0.162 |
(4) Communications skills | −0.004 | 0.367 |
|
−0.017 |
(3) Leadership skills | −0.097 | 0.180 |
|
0.381 |
(5) Understanding cultural and racial differences | −0.283 | 0.118 | 0.211 |
|
(7) Community involvement | 0.342 | 0.094 | −0.093 |
|
(6) Social responsibility and citizenship skills | 0.101 | −0.110 | 0.413 |
|
(15) Sensitivity to the plight of others | 0.366 | −0.054 | 0.258 |
|
The descriptive statistics by factor.
Item | Mean | SD | |
---|---|---|---|
Part 1: benefits of service learning | |||
Factor 1: |
|||
(13) Development of caring relationships | 5.069 | 1.192 | |
(16) Workplace skills | 5.112 | 1.061 | |
(12) Ability to assume personal responsibility | 5.164 | 1.079 | |
(14) Gaining the trust of others | 4.897 | 1.211 | |
(17) Ability to make a difference in the community | 4.603 | 1.179 |
|
(18) Skills in learning from experience | 5.026 | 1.219 | |
Factor 2: |
|||
(9) Problem analysis and critical thinking | 4.603 | 1.257 |
|
(8) Applying knowledge to the “real world” | 4.552 | 1.301 |
|
(20) Connecting theory and practice | 4.647 | 1.232 | |
(11) Conflict resolution | 4.647 | 1.274 | |
(10) Social self-confidence | 4.759 | 1.206 | |
(19) Organizational skills | 4.776 | 1.238 | |
Factor 3: |
|||
(2) Ability to work with others | 5.164 | 1.095 | |
(1) Personal growth | 4.983 | 1.209 | |
(4) Communications skills | 5.000 | 1.165 | |
(3) Leadership skills | 4.569 | 1.113 |
|
Factor4: |
|||
(5) Understanding cultural and racial differences | 4.422 | 1.252 |
|
(7) Community involvement | 4.802 | 1.287 | |
(6) Social responsibility and citizenship skills | 4.974 | 1.226 | |
(15) Sensitivity to the plight of others | 4.879 | 1.188 | |
Part 2: benefits from professional learning | |||
(1) Basis in CS/EE, mathematics, science, and engineering | 4.862 | 0.903 | |
(2) Design/implementation of experiments, analysis, and explanation of results | 4.828 | 0.897 | |
(3) Capability for using programming languages, application systems, and instruments of CS/EE | 4.716 | 0.921 | * |
(4) Capability for hardware and software development | 4.629 | 0.956 | * |
(5) Computer hardware design and computer networks | 4.595 | 0.884 | * |
(6) Teamwork and communication skills | 4.836 | 0.978 | |
(7) Discovery, understanding, integration, and problem-solving in CS/EE | 4.672 | 0.921 | * |
(8) Understanding the impacts of CS/EE on environments and continuous learning | 4.698 | 0.877 | * |
(9) Understanding the ethical and social responsibilities of CS/EE | 4.776 | 0.866 | * |
Part 3: correlation between service learning and professional discipline | |||
(1) The capability and experience gained from service learning assist in learning the professional discipline. | 4.302 | 1.217 | ** |
(2) The capability and experience in the professional discipline assist in the service learning coursework. | 4.181 | 1.269 | ** |
Part 4: personal background | |||
Gender | |||
Male: 86 (74.14%) Female: P 30 (25.86%) | |||
Year | |||
Freshmen: 40 (34.48%) Sophomore: 38 (32.76%) Junior: 4 (3.45%) | |||
Senior: 34 (29.31%) | |||
Major | |||
Computer science: 56 (48.28) Electric engineering: 59 (50.86%) | |||
Place served*** | |||
On-campus: 87 (75%) Off-campus: 56 (48.28%) | |||
Type(s) of services*** | |||
Work assistance: 68 (58.62%) |
|||
Document processing: 36 (31.03%) |
|||
Schoolwork assistance: 14 (12.07%) |
|||
Others: 11 (9.48%) |
To test the hypotheses H1 to H4, we take the Student’s
To test H5, the Pearson correlation between the benefits from service learning and those from professional learning is 0.483. The correlation indicates that the benefits from two types of learning are positively correlated in a moderate manner, so H5 is not rejected.
For
To test
Notably there is one open question in Part 4 “please describe your experiences in service learning in words.” In next section we will describe how to analyze the responses to the open question and how to build the classification model with the personal features and the outcomes of document analysis.
For counseling students on service learning, this section develops the classification model by the naïve Bayesian network. In the naïve Bayesian network, the target variable (root) is students’ perceived benefit level and the predicting variables are measured from the items in Part 4, including personal features and internal responses of the students.
This study uses TFIDF to extract a set of words that are frequent in the students’ answers. At first we select 102 = 100 words. After computing the weight (importance) of the words and eliminating the “stop words” (articles, prepositions, and other auxiliary words), a compact set of 20 words were extracted. These 20 words are further factorized and analyzed, among which six terms are finally selected: “
Nodes of the Bayesian network on service learning.
Node | Description | States |
---|---|---|
|
Level of perceived benefits from service learning | [ |
|
Gender | {1: male, 2: female} |
|
Grade | {1, 2, 3, 4+} |
|
Place of service | {1: on-campus, 2: off-campus, 3: both} |
|
Organization serviced: schools | {1: yes, 0: no} |
|
Organization serviced: NGO | {1: yes, 0: no} |
|
Organization serviced: government(s) | {1: yes, 0: no} |
|
Service type: life assistance | {1: yes, 0: no} |
|
Service type: work assistance | {1: yes, 0: no} |
|
Service type: schoolwork assistance | {1: yes, 0: no} |
|
Service type: escort services | {1: yes, 0: no} |
|
Service type: activities planning | {1: yes, 0: no} |
|
Service type: document processing | {1: yes, 0: no} |
|
Term in feedback: “learning” | {1: significant, 0: not significant} |
|
Term in feedback: “service” | {1: significant, 0: not significant} |
|
Term in feedback: “profession” | {1: significant, 0: not significant} |
|
Term in feedback: “process” | {1: significant, 0: not significant} |
|
Term in feedback: “document” | {1: significant, 0: not significant} |
|
Term in feedback: “capability” | {1: significant, 0: not significant} |
The Bayesian network on service learning benefits.
To estimate the parameters, this study adopts the 5-fold cross validation. The dataset is first divided randomly into five subsamples. Then in every fold, four subsets are input for learning and one subset is used for testing under various scenarios. Before learning the conditional probabilities, the continuous root variable needs to be discretized. We partition the domain of the root into 3 and 5 subranges and compare the classification accuracy by 3 and 5 states, respectively. The results of tests are summarized in Table
The classification accuracy of the Bayesian network.
Fold | 3-state root | 5-state root |
---|---|---|
1 | 0.8189 | 0.3982 |
2 | 0.7931 | 0.4483 |
3 | 0.6897 | 0.4828 |
4 | 0.8276 | 0.5172 |
5 | 0.8966 | 0.5517 |
Average |
|
|
This study investigates the undergraduate students in CS/EE to measure their perceived benefits from the experiences in service learning coursework. By the questionnaire, we explore the significance of the perceived benefits from service learning and the correlation between their professional disciplines.
The results show that the students perceive significant benefits from service learning, which is consistent with the results of [
Besides, we design the classification model using naïve Bayesian networks for predicting the benefits of service learning that students perceive. By linking students’ personal profiles and their feedback on service learning experiences, we may predict their attitudes (level of perceived benefits) toward service learning. Future studies are suggested to enrich the predicting variables from students’ profile and internal responses. Further the classification model can be extended as a knowledge base in the counseling systems for students learning and career path.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors appreciate the anonymous referees for their useful comments and suggestions which help improve the quality and presentation of this paper. Also special thanks are due to the Ministry of Science and Technology (AKA National Science Council), Taiwan, for financially supporting this research under Grant nos. 102-2410-H-259-039- (Han-Ying Kao) and 102-2410-H-141-012-MY2 (Chia-Hui Huang).