January 2012 Volume 15 Number 1 - Educational Technology ...

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Hwang, W.-Y., Shadiev, R., Kuo, T. C. T., & Chen, N.-S. (2012). Effects of Speech-to-Text Recognition Application on Learning Performance in Synchronous Cyber Classrooms. Educational Technology & Society, 15 (1), 367–380. Effects of Speech-to-Text Recognition Application on Learning Performance in Synchronous Cyber Classrooms Wu-Yuin Hwang, Rustam Shadiev, Tony C. T. Kuo 1* and Nian-Shing Chen 2 Graduate Institute of Network Learning Technology, National Central University, Taiwan // 1 Department of Management & Information, National Open University, Taiwan // 2 Department of Information Management, National Sun Yat-Sen University, Taiwan // wyhwang@cc.ncu.edu.tw // rshadiev@lst.ncu.edu.tw // tony@mail.nou.edu.tw // nschen@faculty.nsysu.edu.tw * Corresponding author ABSTRACT The aim of this study was to apply Speech-to-Text Recognition (STR) in an effort to improve learning performance in an online synchronous cyber classroom environment. Students’ perceptions and their behavioral intentions toward using STR and the effectiveness of applying STR in synchronous cyber classrooms were also investigated. After the experiment, students from the experimental group perceived that the STR mechanism was easy to use and useful for one-way lectures as well as for individual learning. Most students also expressed that they were highly motivated to use STR as a learning tool in the future. Statistical results showed moderate improvement in the experimental groups’ performance over the control group on homework accomplishments. However, once the students in the experimental group became familiar with the STR-generated texts and used them as learning tools, they significantly outperformed the control group students in post-test results. Interviews with participating students revealed that STR-generated texts were beneficial to learning during and after oneway lectures. Based on our findings, it is recommended that students apply STR to enhance their understanding of teachers’ lectures in an online synchronous cyber classroom. Additionally, we recommend students should take advantages of the text generated by STR both during and after lectures. Keywords Speech to text recognition, Synchronous learning, Homework, Note–taking Introduction A number of studies have reported the benefits of online synchronous teaching and learning for online courses, although some challenges and limitations still require resolution (Chen, Ko, Kinshuk & Lin, 2005; Hastie, Hung, Chen & Kinshuk, 2010; Wang, Chen & Levy, 2010). One of the most common concerns reported is the presence of poor audio quality due to restricted bandwidth availability and traffic congestion for last mile of Internet access (Chen et al., 2005; Hastie et al., 2010; Wang et al., 2010). According to Chen and Wang (2008) and Kanevsky et al. (2006), students who suffer from bandwidth problems during online synchronous lectures can benefit from reading text streams, which may be synchronously typed on a keyboard or transcribed by Speech-to-Text Recognition (STR) technology. Moreover, Chen and Wang (2008) and Wald (2010) emphasized the pedagogical usefulness of text displayed simultaneously for students during a synchronous lecture, as it facilitates better learning. However, previous research tended to focus on issues related to STR application development and its rate of recognition accuracy improvement, rather than on how it can be applied for improving learning performance (Kanevsky et al., 2006; Wald & Bain, 2008; Way, Kheir & Bevilacqua, 2008). Furthermore, most studies only applied STR in a traditional face-to-face teaching setting but not in an online synchronous teaching and learning environment (Ryba, McIvor, Shakir & Paez, 2006; SRS, 2011; Wald, 2010). This study argues that teaching and learning activities in an online synchronous cyber classroom can be better facilitated by using STR technology. For example, students can follow a teacher’s lecture more easily by reading the STR-generated texts, if the quality of audio degrades during communications; therefore, STR-generated texts can minimize audio communication difficulties and reduce any chance of missing important information. In the interim, STR-generated texts can help students attain a better understanding of a lecture’s meaning, allow for simultaneous note-taking during the lecture, and help students to complete homework after the lecture. An experiment was conducted with the aim to apply STR for improving learning performance in an online synchronous cyber classroom environment as well as in a situation wherein an individual student completed homework. Students’ perceptions and behavioral intentions toward using STR and the effectiveness of applying STR on learning performance in a synchronous cyber classroom were also investigated. This study addressed three ISSN 1436-4522 (online) and 1176-3647 (print). © International Forum of Educational Technology & Society (IFETS). The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page. Copyrights for components of this work owned by others than IFETS must be honoured. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from the editors at kinshuk@ieee.org. 367
primary research questions. First, what are the students’ perceptions and behavioral intentions regarding the use of the STR technology in a synchronous learning environment? Second, do the students who use STR-generated texts perform better at accomplishing homework tasks and in post-test evaluations than the students who do not use STR technology? Third, based on interviews, are STR-generated texts beneficial to students? The remainder of the paper is organized as follows: literature regarding synchronous teaching and learning with STR are reviewed. The theories or models, which are used to analyze the usefulness of the designed STR mechanisms, are discussed. The description of the study’s designed method follows. The results and pedagogical implications of the study are then presented. Finally, a few concluding remarks are given. Literature Review Online Synchronous Teaching and Learning A number of studies have demonstrated the benefits of online synchronous teaching and learning for online courses (Chen et al., 2005; Hastie et al., 2010; Wang et al., 2010). For example, Hastie et al. (2010) argued that online synchronous teaching and learning allows teachers and students to establish their communications link, create a social presence, and negotiate learning content. They then embark on the ‘real’ synchronous interactive component of the lesson and the teacher gives the students ‘live’ feedback and evaluation. According to Chen et al. (2005), “…in many situations synchronous solutions for instruction can outperform both asynchronous online instruction and traditional face-to-face education.” However, advantages of online synchronous teaching and learning can be hindered by certain challenges and limitations. Students in the studies of Chen et al. (2005), Hastie et al. (2010), and Wang et al. (2010) occasionally experienced technological challenges during online synchronous learning caused by network traffic congestion. Specifically, the students could not hear the audio clearly. Chen and Wang (2008) drew attention to text chatting during synchronous teaching and learning by emphasizing that it can accommodate interaction in many important ways. First, text chatting can be used to supplement and complement audio when its quality becomes problematic. Second, text chatting can be used by the teacher or by an advanced student to summarize the major points of verbal exchange to review what has been previously said. Technology of STR and its Accuracy STR technology translates speech input into text in real-time. Way et al. (2008) described STR as a process. This process involves a teacher speaking into a microphone, wherein the speech is recognized and shown synchronously in the form of text for students to read. The accuracy rate is considered one of the fundamental issues in STR studies. Wald and Bain (2008) claimed that the accuracy rate of STR during a lecture depends on a teacher’s lecture experience, abilities, and familiarity with the lecture material. Way et al. (2008) suggested that STR application training should take place in order to achieve good dictation accuracy. Their study demonstrated that the accuracy rate reached 90 percent after moderate STR training. The accuracy rate of application reached 91 percent after the dictionary of STR was customized with unfamiliar domain-specific terminology. STR for Education STR has the potential to become a valuable tool in education as it makes teaching accessible and understandable to all students and it improves the quality of education (Wald, 2010). For example, the Speech Recognition in Schools (2011) project helped students to overcome difficulties in reading, writing, or spelling; the project reported significant improvements in some students’ basic reading, writing, and spelling skills with the support of STR. Ryba et al. (2006) examined STR in the university lecture theatre. Their participants reported that the system had potential as an instructional support mechanism; however, a greater accuracy in the system’s recognition of lecture text vocabulary needs to be achieved. Wald (2010) and Wald and Bain (2008) developed STR applications to assist deaf students and non native speakers during lectures. According to this research, the students perceived that text 368
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Supporting Organizations Centre for
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Analyzing the Learning Process of a
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Angeli, C., & Valanides, N. (2012).
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ased on an integration and evaluati
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ased on the assumption that no sing
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Diagram type D thinking (shown in F
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collaborative task in terms of inte
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Table 4. Descriptive statistics for
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Hong, N. S., Jonassen, D. H., & McG
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commercial off-the-shelf digital ga
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The post-questionnaire was used aft
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Phase Table 1. Phases and activitie
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Research results Describing student
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Assessment results for each one of
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References Annetta, L.A., Minogue,
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Huang, T.-W. (2012). Aberrance Dete
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Detection power Typically, the rela
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Results Detection rates As seen in
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2 .00 .05 3 .02 .10 1 .60 .40 ECI 4
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their easily understandable devices
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Ifenthaler, D. (2012). Determining
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activities is assumed to be reflect
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that the problem representations (i
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deductive reasoning inventory (33 m
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HIMATT structural measures The part
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outcomes, r = .297, p < .01. Accord
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Azevedo, R. (2009). Theoretical, co
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Seel, N. M. (1999b). Educational se
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Initially this paper explores defin
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gamers were boys, and 35% were girl
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Teachers underwent eight hours of p
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A correlation analysis was used to
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playing the game. This would mean P
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Yu, F. Y. (2012). Any Effects of Di
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Considering that identity concealme
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In the nickname group, the student
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Attitudes toward assessors Percepti
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Liu, Z. F., Chiu, C. H., Lin, S. S.
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Appendix A: Attitudes toward Peer-A
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Appendix C: Perception toward the I
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Han, H., & Johnson, S. D. (2012). R
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Research participants The target po
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that its value of cross-loading is
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The canonical variable for the emot
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Literature in the field of online l
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Cornelius, R. R. (1996). The scienc
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Shih, W.-C., Tseng, S.-S., Yang, C.
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To provide personalized (e-)learnin
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Figure 3. An illustrative example o
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elementary schools. Thus they are b
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Table 5. Descriptive statistics for
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References Abowd, G. D., & Atkeson,
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Tseng, K.-H., Chang, C.-C., Lou, S.
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Figure 1. The five stages of KT (Gi
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The use of learning strategies, mon
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Additionally, the above two scales
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Figure 3. Canonical structure betwe
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highly positive perception of CM an
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Langan-Fox, J., Platania-Phung, C.,
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Gömleksiz, M. N. (2012). Elementar
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any statistically significant diffe
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consideration in an educational set
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conditions of a learning environmen
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Kahle, J. B. (1983). The disadvanta
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Appendix A Science and Technology S
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(Guuawardena, Nola, Wilson, Lopez-I
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Questionnaire on student satisfacti
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G4 53 (12.5) 48.9 (13.4) 0.39 G5 51
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across the five levels of knowledge
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Santhanam, R., Sasidharan, S., Webs
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This paper reports only the early e
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Figure 1 illustrates how the abovem
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teacher/instructor continues to dom
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The instructor plays a significant
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Data collection and data sources Fi
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y turning over the responsibility t
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Brophy, J. (1999). Toward a model o
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Lawanto, O., Santoso, H. B., & Liu,
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such as grades and evaluation by ot
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students’ interest, expectancy fo
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constructs that measure students’
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Bandura, A. (1978). Reflections on
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Lin, J. M.-C., & Liu, S.-F. (2012).
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Table 1. The MSWLogo commands learn
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other words, we had prepared a set
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lab. When she returned and found th
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Attitudes toward Programming and Co
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Table 5 successfully, it took four
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Kuter, S., Altinay Gazi, Z., & Alti
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not only the means for trainees’
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Data Collection Techniques and Anal
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organizations was highlighted by on
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provides the means for professional
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Kohonen, V. (2001). Towards experie
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As Rogers (1995) postulates in his
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Instrument and data collection Afte
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“Raising our computer skills in C
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teachers deploy ICT tools in langua
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Ma, W., Anderson, R., & Streith, K.
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APPENDIX A. Questionnaire for Dista
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consistency with other ideas, and a
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conceptions, justifying their belie
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each group. The experimental group
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specifically, instructional approac
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Table 3 shows that the mean frequen
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previous Web-based instructional le
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Millar, R., & Osborne, J.F. (1998).
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Several studies explore the roles t
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understand the content structures o
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teacher was not allowed to provide
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two coding schemes, as illustrated
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also very limited. Teachers and sof
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learning activities. British Journa
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2009; Bernard & Cathryn, 2006) or p
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In this study, the focus of the ins
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The student’s degree of mastery i
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The questionnaire for the acceptanc
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the t-test result, it is found that
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Scale Questionnaire item Mean S.D.
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Hwang, G. J. (2003). A conceptual m
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sharing, problem solving, and achie
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Virtual learning environment In the
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Class Table 1. The survey questions
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“…the distance and the lack of
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collaboration within EVS, thus prev
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Huang, T.-H., Liu, Y.-C., & Chang,
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y personalised context examples in
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Students’ Problem-Solving Guidanc
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Research Method Figure 8. The scree
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Table 4. Abstract of Pairwise Compa
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questions designed for the system h
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Lee, J. (2012). Patterns of Interac
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This study focused on online fora i
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Results and Discussion Due date-cen
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In addition, all groups developed 8
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future. Therefore, they had an oppo
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surprising that most interaction wa
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Pawan, F., Paulus, T. M., Yalcin, S
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systems by using 18 personalization
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Short-Term Sensory Memory is a temp
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with respect to articles by using a
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system also computes a review value
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DFL. d ( yi ) is the degree of mem
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the experimental group can understa
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Finally, Figure 10(A) and Figure 10
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Appendix # Question Description Par
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mental models, are cognitive struct
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Based on the theoretical implicatio
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Data analysis To test the model fit
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may be considered an effective inte
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Hsu, I.-C. (2012). Intelligent Disc
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(Gradinarova, Zhelezov et al. 2006)
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RLO denotes a remote learning objec
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Figure 2. The flow-oriented LOFinde
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if x include with y, and y include
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Similarly, the relation metadata of
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(defrule student-advisor (triple (p
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References ADL. (2006). Sharable Co
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determinants of how people think, b
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Page 333 and 334: Learning is a cognitive activity th
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Page 341 and 342: Conclusion Conducted research showe
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Page 347 and 348: Reflection Types This study attempt
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Page 351 and 352: Results Learning Outcomes Pre- and
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Page 365 and 366: Collaboration Questionnaire results
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Page 383 and 384: References Bloom, B. S. (Ed.). (195
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Page 387 and 388: understand how we can effectively u
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Page 397 and 398: Conclusion This study provides a so
Page 399: Smith, H. J., Higgins, S., Wall, K.
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