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What Matters?An Investigation of Student Collaborative E-learning Ren Ding1 School of Information Management Wuhan University Wuhan, P.R.China 1 [email protected] Jiangping Chen2 & Ryan Knudson3 Frank Braun4 College of Informatics University of North Texas Denton, U.S.A 2 [email protected] 3 [email protected] University of Northern Kentucky 4 [email protected] Abstract—This paper reports a study conducted to understand students collaborative e-learning behavior within a team project in a graduate-level class. The effectiveness of collaborative elearning is measured, and the factors affecting it are identified. The study finds that cognition, information communication, IT skill, member relationship, instructor guidance and supervision have a significant impact on collaborative e-learning. This research proposes a primitive model for collaborative e-learning. It will help educators to better design collaborative e-learning courses. Keywords-collaborative e-learning; collaboration effectiveness assessment e-learning factors; I. INTRODUCTION In this age, effective collaboration is essential, especially in virtual environments. Currently, a growing number of education institutions are offering Internet or online classes that provide more flexible learning than traditional face-to-face classrooms. One common method of teaching students to collaborate effectively is the assignment of team projects. However, conducting team projects in online classes poses challenges to both students and instructors: students in a team may have different ideas on selecting collaborative learning methods, and the instructors have fewer opportunities to monitor team activities and to directly advise the students [1]. A first step toward better design and supervision of collaborative e-learning is to better understand the experiences of students in team projects, especially by measuring their elearning effectiveness [2] and identifying methods to do so. II. COLLABORATIVE E-LEARNING Collaboration has been deemed among the “most prominent 21st-century skills”, and “a central piece in the education puzzle” [3]. Collaborative e-learning is defined as “constructing knowledge or solving problems through mutual engagement of two or more learners in a coordinated effort using Internet and electronic communications for their interactions” [4]. During collaborative e-learning, each student contributes to and learns from the project on which they are working. Traditionally, instructors have thought about the assessment of student collaborative e-learning effectiveness in terms of examination grades. Today, when the aim of e-learning is cultivation of students’ self-directing capabilities [5], the evaluation of collaborative e-learning needs to further consider students’ perceptions towards their e-learning experiences. Noble therefore proposed Cognitive-Based metrics to evaluate effective collaborative learning [2]. Cognitive-Based metrics are theoretically supported by the Cognition-Behavior-Product model and the Transactive Memory model. The CognitionBehavior-Product model demonstrates that individual cognition affects team behavior, therefore it emphasizes the importance of measuring students’ collaboration cognition and both team and individual collaborative learning outcomes. The Transactive Memory model addresses the necessity of measuring students’ communication. In this model, transactive knowledge or memory, which appears to be a powerful intervening variable for collaborative e-learning, is distributed throughout the team. Swan noticed two pivotal points for the evaluation of collaborative e-learning effectiveness: learning goals, and both individual and group learning outcomes [6]. In our study, the expected goals for individual students in collaborative e-learning include: (1) to increase student knowledge about collaboration and the course content; (2) to promote personal growth; and (3) to cultivate student interest in collaborative learning. In the same vein, the expected goals for teams are to increase (1) team collective capability; (2) team learning ability; and (3) team members’ confidence in and satisfaction with the team [7]. We will study students’ perceptions about these goals based on the actual outcomes of the collaboration to explore the students’ learning effectiveness. The outcomes of collaborative e-learning will be measured by the combination of both team outcomes and individual outcomes. III. AFFECTING FACTORS Collaborative e-learning can be influenced by many factors. The impact of e-collaborative IT technologies on e-learning is evident. Padilla [8] observed that students who demonstrated proficiency in IT skills have more interest in distance learning and usually contribute more to the team work than those who are not as proficient. Hodgkinson [9] believed that trust and relationship among team members are the strongest factors of collaborative e-learning effectiveness. Haycock [10] examined several critical success factors for student collaborative learning effectiveness. He clustered those factors into 6 categories: factors related to the environment, factors related to membership characteristics, factors related to process and structure, factors related to communication, factors related to purpose, and factors related to learning resources. Based on the literature review, our study selected and tested ten factors, as shown in Table 1. TABLE 1. THE TEN FACT ORS AND THEIR EXPLANAT IONS Factors Collaborative e-learning cognition (CC) Information communication (IC) IT skills (IS) Member trust (MT) Trusting members and their knowledge Team leadership (TL) Guidance & supervision (GS) Commitment of members (CM) Member relationship (MR) Pre-collaboration experience (PE) Project development process (PD) IV. Explanations Understanding the importance of collaborative e-learning and willing to collaborate Open and frequent communication among members Adept at choosing and utilizing IT tools for collaborative e-learning Leadership of team coordinators Guidance and supervision provided by the course instructor Other members’ dedication to the team project Relationships among team members Prior collaboration experience H2: information communication (IC) positively affects the outcomes of collaborative e-learning. H3: IT skills (IS) positively affect the outcomes of collaborative e-learning. B. Research Procedure A survey was designed based on the research model. The survey instrument was adapted from an online questionnaire (http://www.drbiz.com). Each question item was measured using a seven-point Likert-type scale. Fifty-nine graduated students who took a blended course (SLIS 5740: Introduction to Digital Libraries) at the Department of Library and Information Sciences, University of North Texas took the survey. The class had a one-day face-to-face meeting during the semester, with the rest of the teaching online. Blackboard Vista (http://www.blackboard.com/) was used as the E-learning platform. Students are required to conduct a team project for the course. Each team has 3-4 team members and a team coordinator nominated by the members. The survey results were analyzed by SPSS software. Cronbach’s alpha of team outcomes and individual outcomes is 0.84 and 0.91 respectively, and Cronbach’s alpha of affecting factors is 0.77. An appropriate pace of team project development RESEARCH MODEL AND PROCEDURES A. Research Model This study aims to investigate student behavior within a collaborative e-learning environment. The research objects are to evaluate students’ learning effectiveness and to determine the factors affecting their e-learning. The research model is illustrated in Figure 1. It is established by adapting the Cognition-Behavior-Product and Transactive Memory models. V. FINDINGS A. Collaborative E-learning Assessment The individual collaborative e-learning outcomes of the students are positive. 84.2% students believed they learned useful knowledge by participating in the team project. 68.4% felt they were offered many opportunities for personal growth. Concerning team outcomes, 82.4% students felt that their own team demonstrated good collective capability, which indicates that the cohesion of the team project was relatively strong. Team projects provided a positive learning atmosphere according to 88.0% of students, and 73.7% students felt confident in their teams. By comparing student learning outcomes and expected goals, the effectiveness of this elearning can be evaluated. Such evaluation reveals that most students feel they can effectively achieve positive learning results and reach educational goals in e-learning, shown in Table 2. TABLE 2. COLLABORAT IVE E-LEARNING OUT COMES Mean Agree and Strongly Scale Items (1-7)a Agree Percentage Individual outcomes Team outcomes Figure 1. Research Model Ten alternative hypotheses will be tested, as labeled in Figure 1. Three of the ten hypotheses are listed here: H1: collaborative e-learning cognition (CC) positively affects the outcomes of collaborative e-learning. a Learning useful knowledge 5.40 84.2% Growth opportunities 4.96 68.4% Future participant willingness 4.28 56.1% Collective capability 5.47 82.4% Learning atmosphere 5.76 88.0% Team confidence 5.09 73.7% A seven-point Likert scale from 1=”strongly disagree” to 7=” strongly agree” E-learning cannot be carried out without sufficient communication channels. As Table 3 demonstrates, E- mail is mostly used (59.4%), preferred (50.0%), and perceived to be the most effective (37.5%) communication channel. The discussion forum within the class site in Blackboard was judged as the second most effective channel (18.8%). Interestingly, traditional face-to-face communication was considered only the third most effective communication channel. TABLE 3. COMMUNICAT ION CHANNEL Communication Channels Mostly Mostly Most used preferred effective Face to Face 0.0% 6.3% 15.6% Telephone 0.0% 3.1% 6.3% Conference calls 0.0% 9.4% 9.4% E-mail Discussion forum 59.4% 28.1% 50.0% 15.6% 37.5% 18.8% Online chat 6.3% 12.5% 9.4% Wikis (Google Docs) 6.3% 3.1% 3.1% B. Crucial Factors To test the ten hypotheses, correlation and multiple regression analysis were conducted. Correlations were measured between the ten independent variables listed in Table 1 and the dependent variable: the outcome of collaboration. The results demonstrate that CC(r=0.433), IC(r=0.684), GS(r=0.608), IS(r=0.787), TL(r=0.182), MT(r=0.214), MR(r=0.701), CM(r=0.332), and PD(r=0.384) have significant associations with the dependent variable. The coefficients among independent variables are low (r<0.7), which indicates little inter-correlation with multi-collinearity among independent variables. Table 4 reveals the results of multiple regressions. TABLE 4. AFFECTING FACTORS Model Standardized coefficient Beta p -0.548* Constant CC t 0.110 Hypotheses 0.059 ** 0.024 H1(accepted) ** H2(accepted) 4.230 IC 0.321 2.194 0.040 IS 0.226 5.213** 0.000 H3(accepted) MT 0.006 0.027 0.979 H4(not accepted) TL 0.201 1.410 0.180 H5(not accepted) GS 0.150 2.721** 0.047 H6(accepted) CM 0.018 0.115 0.910 H7(not accepted) MR 0.221 4.570** 0.000 H8(accepted) PE -0.004 -0.021 0.984 H9(not accepted) PD -0.095 -0.521 0.611 H10(not accepted) R2=0.914 Adjusted R2=0.796 F-ratio=21.195 (sig=0.000) * P<0.10 ** P<0.05 According to the results, collaborative e-learning cognition, information communication, IT skills, instructor guidance & supervision, and member relationship are the crucial factors affecting collaborative e-learning. H1, H2, H3, H6 and H8 are accepted, while the remaining hypotheses are rejected. VI. DISCUSSION A. Information Communication Differing from other patterns of e-learning courses, collaborative e-learning emphasizes students’ information sharing and mutual communication within a team. As the most important affecting factor, information communication should be paid most attention. Open and frequent information communication will bring about deep understanding and interaction among student as well as abundant knowledge sharing opportunities [11]. In an e- learning environment, information communication activities depend on the type of communication media and the channels through which they are conducted. Teaching students to use appropriate communication tools or channels will be beneficial for pedagogy [12]. Our study suggests that E-mail is the preferred communication channel for the majority of students, as proposed by Padilla- Meléndez [8]; however, other types of communication channels are also utilized by students and shouldn’t be ignored. Educators should encourage face-to-face meetings even in an e-learning environment. Some students indicated that having a face-to-face meeting at the beginning of the course would encourage learning [13]. Totally abandoning this direct communication approach may obstruct students’ learning effectiveness. B. IT Skill Web-based technology is a powerful tool for supporting collaborative learning [14]. IT skill has proved to be an important factor influencing collaborative e-learning outcomes. A person who is proficient in IT skills may enjoy e-learning. However, for those who are less skilled, it may engender more struggles and challenges. Web2.0 has brought about a number of IT tools supportive of collaborative learning work, such as Google Docs, Wiki, and social networking sites. Although these tools were not originally designed for collaborative elearning, they can be tailored by users to assist e-learning. When observing the student communication channels, which contain some IT tools, the students’ preference for using these innovative tools is evident. This demonstrates that introducing these new tools to students and cultivating their interests in using them for course-related projects are important. C. Member Relationship Member relationship reflects the degree of student mutual respect, understanding, and ability to compromise [10]. Since an e-learning environment provides less face-to-face contact opportunities among students, students are likely to avoid establishing relationships with unfamiliar members, a phenomenon called “escape” [15]. Such inactive member connection will impair the dynamics within a collaborative learning culture. Our study results illustrate that member relationship has a relatively strong association(r=0.701) with the outcomes of collaborative e-learning. Member relationship also has associations with other factors, such as information communication, member trust, and commitment of members. Therefore, it seems important to devote attention to cultivating student member relationships in an e-learning environment. D. Instructor Guidance Supervison Arguments are abundant about the role of educators in an e-learning environment. Some believe that educators should monitor students less to encourage their self-learning; while others state that more guidance and supervision are necessary. For instance, Woods [16] reported that online learners often felt isolated from instructors due to a lack of connection. Our study tests the power of instructor guidance and supervision, and proves that it is an important factor affecting collaborative e-learning. Suitable guidelines and oversights from the instructor will offer a clear learning aim and rules, provide review for team work in progress, and help create the climate of a learning community for the students. E. Collaborative E-learning Cognition In the Cognition-Behavior-Product model, collaboration behavior and outcomes are decided by collaboration cognition. Our study confirms the important status of cognition by proving the hypothesis that collaborative e-learning cognition significantly influences student outcomes. Being aware of the importance of collaborative e-learning will promote student collaborative learning consciousness and make student engagement in this type of course more zealous. In education practice, instructing students in the importance of collaborative learning and encouraging students to implement such cognition into learning activities seems appropriate. The results show that member trust, team leadership, commitment of members, project development process, and pre-collaboration experience do not significantly affect collaborative e-learning outcomes. This is an interesting discovery. For many students, an e-learning environment reduces their concerns for other students - they may care more for their individual gain [14]. That might explain in part why member trust, team leadership and commitment of members do not have much impact on student outcomes. Students may desire more supervision from instructors rather than from team leaders or fellow members. However, member trust, team leadership, commitment of members and project development process still have associations with collaborative e-learning outcomes, and they might have indirect influence on student collaborative e-learning outcomes. When educators design and implement a collaborative e-learning course, the five factors, including information communication, IT skill, member relationship, instructor guidance & supervision, and collaborative e-learning cognition should be paid closest attention, followed by other factors, including member trust, team leadership, commitment of members and project development process. VII. CONCLUSION This study examined the outcomes of student collaborative e-learning and assessed student learning effectiveness. Five factors affecting collaborative e-learning outcomes were defined and identified. These factors are information communication, IT skill, member relationship, instructor guidance and supervision, and collaborative e-learning cognition. According to the regression analysis results, information communication is the most influential factor, and IT skill is the second most influential factor. 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