April 2012 Volume 15 Number 2 - Educational Technology & Society

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Previous studies have explored the factors that influence the use and acceptance of e-learning systems. Ozok, Fan, and Norcio (2010) reported a significant positive correlation between a system’s predictive consistency and the accuracy of its recommendations and noted that learners considered system reliability to be an important factor in their use of a system. Interface satisfaction refers to the convenience experienced by learners when using a system insofar as it conforms to the operational habits of learners (Nielsen, 1993; Rushieks & Rushineks, 1986). Chien (2009) noted a significant positive correlation between interface satisfaction and content satisfaction. E-learning systems must offer stability and user-friendly interface to continue to attract learners, motivate them to visit learning websites, and explore the relationships among predictive factors and systems-management issues in the affective and cognitive reactions of learners (Shneiderman, 1997). Literature using specific reading or instructional materials as indicators of use intentions (Chen & Yeh, 2008; Hu & Pu, 2009; and Kuan, 2004) has found a significant positive correlation between recommendation accuracy and intent to use e-learning resources. Recommendation accuracy refers to whether the suggested content satisfies learners’ demands and influences their future use intentions. A study conducted by Chang (2010) on the use and acceptance of mobile learning communities found that satisfaction with the educational content affected acceptance of the system. Thus, the accuracy of the recommendations and the satisfaction with the content offered by e-learning systems can be investigated as measurable variables (Chang, 2010). Indeed, Baker-Eveleth, Eveleth, O’Neill, and Stone (2006) examined laptop exams; Cheng-Chang, Gunter, Sivo, and Cornell (2004–2005) explored e-learning management systems; and Ndubisi (2006) analyzed the online learning community’s acceptance of a particular system. According to research performed by Simonson, Smaldino, Albright, and Zvacek (2000), the following four important factors enhanced learners’ use of information technology: learners’ attitudes, experiences, cognitions, and learning styles. Of these factors, learners’ attitudes were found to be the most important indicator of satisfaction. After gaining learners’ acceptance, a learning system should meet learners’ demands by increasing its functions to enhance learning. Hypothesis 1: Five factors affect use intention with web-based systems designed to recommend interdisciplinary educational resources to learners: system stability, interface satisfaction, recommendation accuracy, content satisfaction, and system acceptance. The 3-TUM research framework proposed by Liaw (2007) is currently widely used in many important studies on learners' attitudes (Lai et al., 2009; Liaw & Huang, 2003; Liaw et al., 2007). The major conceptual foundation of this approach draws on SCT, TPB, and TAM to explore the attitudes of the users of information technology at three different levels: individual experiences and system quality, affective and cognitive reactions, and behaviors and intentions. The level of individual experiences and system quality identifies the events that can influence affective and cognitive reactions. Quantification at the affective and cognitive level allows for predictions about intentions and behaviors (David et al., 1989; Liaw, Chen, & Huang, 2008; Triandis, 1971). In other words, individual experiences and system quality can positively influence affective and cognitive experiences. At the same time, affective and cognitive dimension can also positively impact behaviors and intentions. Extant literature shows that system stability and interface satisfaction were included in the category of system quality, whereas recommendation accuracy and satisfaction with content constituted cognitive factors, and behaviors and intentions were related to system acceptance. Hypothesis 2a: System stability and interface satisfaction can positively predict recommendation accuracy and content satisfaction. Hypothesis 2b: Recommendation accuracy and content satisfaction can positively predict system acceptance. Development of a web-based learning system for recommending interdisciplinary learning resources During the process of e-learning, learning guides help each learner navigate through materials according to their abilities, interests, habits, and needs. Traditional classroom teaching uses the same schedule for all learners under the assumption that all have similar capabilities. Learners using recommendation services are regarded as unique individuals with particular learning abilities and preferences. They are allowed to use materials at their own pace and are expected to have personalized learning experiences. Moreover, the system records every completed learning process to enable understanding of the learning schedule and status of each learner as well as to provide learners with clear insight into their own status as learners. 91
Figure 1. The framework of interdisciplinary recommendation learning service system The proposed recommendation service is presented in Figure 1. Learning objects are transformed into vector-based representations through a learning-object extractor and are then stored in the learning-object domain. The learningcontent-management subsystem (LCMS) stores the learning objects in the database in the form of a shared contentobject reference model (SCORM). Learning objects can be in the form of text, graphics, videos, and simulation clips, which enable their repeated use as the system expands to include additional learners. Initially, new learners will be guided to a survey to examine their interests, and these data will be used to establish individualized learning profiles. Previous users will be sent directly to the learner-profile domain to obtain their personal profiles. Next, the system’s recommendation operations will identify the appropriate learning objects. Learning objects with the highest scores will be recommended to learners, as shown in Figure 2. The following four courses will be saved in the database of the system: basic computer concepts (BCC), databases, networks, and web design. This study classified these courses according to the characteristics of their teaching materials. The difficulty of materials was divided into low, medium, and high. The time required to learn the materials was classified as within 30 minutes, 30–60 minutes, and more than 60 minutes. The types of teaching materials included introductory, theoretical, practical, and mathematical. Introductory material presents foundational concepts, theoretical material teaches logic or epistemology, practical material involves the use of software, and mathematical material involves methods of calculation. The extended object-oriented portal system (XOOPS) was selected as the infrastructure for this web-based service. Figure 2. Screensnap of an interdisciplinary web-based recommendation learning service system The recommendation function is key success factor to recommend learning service system which mainly introduces the advantages of explicit and implicit ratings to proceed a design. A cross validation is conducted on the data 92
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April 2012 Volume 15 Number 2
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Supporting Organizations Centre for
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Contextualizing a MALL: Practice De
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Kop, R. (2012). The Unexpected Conn
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Human mediation and information flo
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elevant information based on some k
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of learners. A search facilitated t
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information as human mediation mean
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Blau, I., & Barak, A. (2012). How D
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examined whether the readiness to p
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Table 5 presents the ANOVA for the
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However, in discussing a non-sensit
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deviations showed smaller variance,
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actual participation discussing sen
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Mesch, G., & Elgali, Z. (2009). Soc
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Although prior research on overall
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with a score of 39 and below as “
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Table 2 demonstrates mean scores an
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Discussion Although the recent Inte
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Furthermore, the most preferred pla
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MEB. (2009). Internete erisim proje
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Learning Management Systems (LMS) b
Page 45 and 46: practice and repetition with feedba
Page 47 and 48: Online peer assessment Peer assessm
Page 49 and 50: At an international conference on m
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Page 53 and 54: Meishar-Tal, H., & Tal-Elhasid, E.
Page 55 and 56: Tsai, S. C. (2012). Integration of
Page 57 and 58: order to decrease text complexity a
Page 59 and 60: matter how the learners’ aptitude
Page 61 and 62: If compared by programs, WP student
Page 63 and 64: mean F2F 4.64 4.52 4.35 4.44 *: p
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Page 67 and 68: Chen, G.-D., Lee, J.-H., Wang, C.-Y
Page 69 and 70: and diverse cognitional and emotion
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Page 73 and 74: Materials and procedure Each studen
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Page 87 and 88: Utilization of Test Item 36 33 30 2
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Page 113 and 114: Based on this connection, we consid
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Page 135 and 136: Method Sample and procedure Figure
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Page 141 and 142: Brown, M., & Cudeck, R. (1993). Alt
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paper focuses on the first three st
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their self-esteem to be partners in
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Democratizing knowledge and symmetr
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1) Reading the article: At the star
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Effects of RCKI principle-based ped
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References Alexander, C. (1979). Th
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Valsamidis, S., Kontogiannis, S., K
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The Analog system (Yan et al., 1996
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First, the number of the sessions a
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BioLayout uses a modified version o
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As shown in figure 3, all metrics c
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Figure 7. The detailed results for
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Enright, A. J., van Dongen, S., Ouz
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Chu, S. K. W., Kwan, A. C. M., & Wa
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teachers, as well as collaboration
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from the students’ ratings appear
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Table 5 summarizes the students’
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eading blogs (i.e., reminders, easy
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Luehmann, A. L. (2008). Using blogg
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Uzunboylu et al. (2009) examined th
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System implementation Overview of M
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Methods Before the experiment, a mo
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too simple, incomplete, or they eve
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Comparisons of UMLS Questionnaire i
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already described may play an impor
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Parker, D., Manstead, A. S. R., Str
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Avci, U., & Askar, P. (2012). The C
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Literature review The related varia
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collected through Personal Informat
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Self-efficacy 92 5 21 13.37 3.726 W
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In this problem, intention was take
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References Ajjan, H., & Hartshorne,
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Tan, T.-H., Lin, M.-S., Chu, Y.-L.,
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Students perused the course materia
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problem. All articles were then sen
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tests, electronic tests are more li
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Students 10 and 11: My classmate Ed
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collection was also approved by all
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Ubiquitous Revision Seamless Collab
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Tai, Y. (2012). Contextualizing a M
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tasks were developed from task type
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experiences in this phase. Moreover
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test is administered again right af
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The collected data were analyzed wi
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Gorp, K. V., & Bogaert, N. (2006).
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4PL IRT model According to the numb
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examinee’s ability more accuratel
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would be more accurate if items j+1
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C�I or Change I�I) would be 0.2
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improved by rearrangement procedure
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Table 6. Repeated Measures ANOVA of
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Leppisaari, I., & Lee, O. (2012). M
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Selection of the virtual tool The t
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Environmental themes emerged strong
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Visualization of text (visual langu
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environments where visual technolog
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- Logging in to learning environmen
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McNaught, C., Lam, P. & Lam, S.L. (
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on-site to finally achieve a meanin
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Table 1. Digital game designers and
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worldviews are socio-cultural-histo
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Phase Methods Visitor data collecti
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operate the game without the help o
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Visitors (end users) must be heard.
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Islas Sedano, C., Pawlowski, J., Su
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part of something larger than the s
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Previous studies have developed rel
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Technology Acceptance. The 10 items
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Table 4. Model Fit Indices Model χ
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In the final path model of set 1, t
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Discussion Differing from prior stu
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Haythornthwaite, C., Kazmer, M. M.,
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Ge, Z.-G. (2012). Cyber Asynchronou
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college of a university situated in
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Table 3 shows us the following info
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Appendix B shows Class 1’s respon
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increase one’s ability to process
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Appendix A Responses concerning syn
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Shih, S.-C., Kuo, B.-C., & Liu, Y.-
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Adaptive U-learning Math Path Syste
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Figure 4. Online tutorial courses o
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Participants and Experimental Proce
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the influence of Test1 scores, an e
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Hall, T., & Bannon, L. (2006). Desi
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(Hofer & Pintrich, 1997; Liu, Lin &
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� Through online peer assessment,
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elativism.” High Internet self-ef
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Peng, H., Tsai, C.-C., & Wu, Y.-T.
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Since game-based learning has been
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The second reason is about the sust
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Methods Figure 5. Quest NPCs provid
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These differences indicated that pa
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quests further involves students’
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Chang, I.-H. (2012). The Effect of
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(1) Vision, planning and management
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Based on an examination of the lite
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Table 2. Analysis of reliability an
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λ10 - 0.69 0.83 ε5 14.50 * - 0.32
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Bailey, G. D. (1997). What technolo
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Stegall, P. (1998). The principal:
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instruction has studied the student
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espond and the estimation of possib
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Research question For the purpose o
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READI reliability To verify the con
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Physical -1.43333(*) .44699 .029 -2
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The results of this study suggest t
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Hsu, Y.-C., Ho, H. N. J., Tsai, C.-
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domains. Five research questions we
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Research sample groups (4) Faculty
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13. Policies, Social Culture Impact
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Non-Specified Others Engineering &
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(AR = -2.1) (n = 7) 2. Non-specifie
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(n): Total number of articles Resea
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Compared to the publications in 200
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Pedagogical Content Knowledge (TPAC
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