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Computers & Education 68 (2013) 345–352
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Computers & Education
journal homepage: www.elsevier .com/locate/compedu
Investigating students’ learning approaches, perceptions of onlinediscussions, and students’ online and academic performance
Silvia Wen-Yu LeeGraduate Institute of Science Education, National Changhua University of Education, Changhua 500, Taiwan
a r t i c l e i n f o
Article history:Received 27 December 2012Received in revised form12 April 2013Accepted 21 May 2013
Keywords:Asynchronous online discussionsLearning approachesPerceptionsContributions
E-mail address: [email protected].
0360-1315/$ – see front matter � 2013 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.compedu.2013.05.019
a b s t r a c t
The main purpose of this study was to understand the relationships between students’ approaches tolearning, their perceptions of online discussions, students’ contributions in asynchronous discussions,and their academic performance. Two sets of questionnaires were used for understanding students’approaches of learning and perceptions of online discussions. The online postings from seven weeks ofdiscussions were coded into three major categories: Initiation, Elaborated Response (ER), and Responsewith Resources (RWR). The results showed, first, some aspects of students’ perceptions influenced thenumbers of ERs and RWRs. Secondly, students’ contributions to Initiation messages and RWR signifi-cantly related to deep motivation and deep strategies; however, the numbers of these two types ofmessages were negatively correlated to surface strategies. Finally, cluster analysis revealed three distinctgroups who scored significantly different in almost all aspects of approaches to learning and perceptionsof online discussions. Students in the cluster who adopted deep approaches and scored highest in theperception scales outperformed students in the other two clusters, both in terms of the number of ERmessages and academic performance. Pedagogical implications for teaching with online discussions arediscussed in this study.
� 2013 Elsevier Ltd. All rights reserved.
1. Introduction
Internet has been playing an increasingly important role in higher education (e.g., Jones, Johnson-Yale, Millermaier, & Pérez, 2008).Asynchronous online discussion is one of the most used Internet-based technologies in higher education (Jacobsen, 2006; Luppicini, 2007;Zhan, Xu, & Ye, 2011). Taken Taiwan for example, according to the survey conducted in 2005, about 80% of surveyed institutes of highereducation have adopted Internet-based asynchronous communications for teaching (National Science Council, 2006). Asynchronous onlinediscussion is used either for encouraging reflection as a complementary method to face-to-face teaching (e.g., Zhan et al., 2011), or it is usedas a major means for communication in distance learning (e.g., Lee & Tsai, 2011a). The commonly reported advantages of online asyn-chronous discussions included promoting thoughtful and reflective content in the discussion, promoting active learning or self-regulatedlearning, encouraging critical thinking, and supporting collaborative knowledge construction (Lee & Tsai, 2011a; Vighnarajah, Luan, &Bakar, 2009; Wang & Woo, 2007; Yeh, 2010). However, researchers and practitioners still observed low contribution rates or lack ofengagement in online asynchronous discussions at various contexts (Hara, Bonk, & Angeli, 2000; Hew, Cheung, & Ng, 2010). Past researchdiscussed the impact of intervention factors, such as use of grades, use of posting guideline, the time-span of the discussion, peer facilitation,and instructor’s participation andmediation (Hew & Cheung, 2008, 2011; Hew et al., 2010; Kopp, Matteucci, & Tomasetto, 2012; Mazzolini &Maddison, 2007). Although these guidelines and facilitation provide structures and external motivation for some participants, they did notseem sufficient in promoting autonomy or sustained engagement for all the participants. Thus, past studies emphasized the needs to lookinto other factors, such as the social aspects, intrinsic motivation, emotions, and experiences (De Laat & Lally, 2003; Ellis, Goodyear, Calvo, &Prosser, 2008; Vonderwell, 2003).
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In this study, we examined the potential factors at two levels together. At a level specific to online learning, past research hasshown the importance of understanding students’ perceptions of online learning when students participated in online activities (Lee &Tsai, 2011b). In order to further investigate how students perceive different aspects of online discussions in relation to learning, a newquestionnaire was designed for this purpose. Because online discussions were part of the class requirements, at another levelregarding learning in general, students’ overall learning approaches toward the subject area could influence students’ participation inthe discussions online. Past research has shown that learning approaches in conjunction with other factors can probably betterpredict students’ academic performance in online learning than learning approaches alone. Thus far, only a few studies linked stu-dents’ perceptions of online discussions, or motivation and strategies of online discussion to students’ actual online contributions(e.g., Xie, DeBacker, & Ferguson, 2006). Therefore, this study looks into students’ perceptions of online discussions and overalllearning approaches in relation to their actual contributions, manifested as posting numbers, in an asynchronous message board.Furthermore, studies examining the aforementioned relationships in the science disciplines are even rare. The objectives of this studyare as follows:
1. To validate the Perceptions of Asynchronous Online Discussion (PAOD) Questionnaire2. To explore the relationships between students’ perceptions of online discussions and students’ contributions of different types of
messages3. To explore the relationships between students’ approaches to learning and students’ contributions of different types of messages4. Through comparing different clusters, to investigate the influence of both students’ perceptions of online discussions and their ap-
proaches of learning on students’ overall performance in the course and students’ online contributions
2. Literature review
2.1. Perceptual and psychological factors of online discussions
Students’ contributions in online discussions may be attributed to various factors. One fundamental reason may relate to how studentsperceived the affordance of the asynchronous communication tools. Students who have realized the affordance of online communicationtools, such as the affordance of communicating at any time and the affordance of being able to review past messages, tended to useasynchronous discussion voluntarily (So, 2009). Researchers also explored students’ personal psychological status that may influencestudents’ participation in online discussions. In Rahman, Yasin, Yassin, and Nordin’s (2011) exploratory study, they synthesized responsesfrom open-ended questions and concluded that self-esteem, self-efficacy, psychological resistance and academic anxiety were the majorthemes associated with students’ feelings of online discussions. Since Rahman’s et al. (2011) used a qualitative method, further research isrequired to validate these preliminary factors and understand their relationships to actual postings.
Students’ understanding of the purpose of online discussions or the value of contributing online is another important factor. Afterreviewing about 50 empirical studies, Hew et al. (2010) concluded that one of the major reasons for limited student contributions is “notknowing the need of online discussions”. When students see the discussion topics directly related to the curriculum (Guzdial & Turns, 2000)or when students were instructed the purposes of the online discussions (Jung, Choi, Lim, & Leem, 2002), they tended to contribute more.
Finally, students’ level of motivation plays an essential role (Rahman et al., 2011). Students who were intrinsically motivated tendedcontributed more in online discussions than students who were extrinsically motivated (Xie et al., 2006). Xie and Ke (2011) measured fiveaspects of motivation and studied their relationship to students’ online behavior. Their results showed students’ feelings of competence, andfeelings of autonomy significantly related to lower-levels of interactions (e.g., sharing information); students’ feelings of relatedness pre-dicted higher-levels of interactions (e.g. knowledge construction; Xie & Ke, 2011).
Overall, research showed psychological or perceptual factors do have some impact on students’ online contributions, however, theconstructs from relevant studies diverse. Therefore, comparisons between studies have become more difficult. More research is needed tostart developing a model for the relationships among participants’ psychological or perceptual factors variables, and outcome variables ofonline discussions.
2.2. Approaches to learning in relation to online discussions
Students’ approaches to learning are defined as “the ways in which students go about their academic tasks, thereby affecting the natureof the learning outcome (Biggs, 1994, p. 318).” Deep approach is driven by learners’ intrinsic motivation and learners tend to appropriatelyengage the task in order to maximize understanding (Biggs, 1993, 1994, p. 320). Surface approach, on the other hand, is driven by extrinsicmotivation. Thus learners usually manage to invest minimal time and efforts to meet the minimal requirements (Biggs, 1993, 1994, p. 320).Researchers have investigated students’ approaches to learning in online and face-to-face discussion setting (Ellis, Goodyear, Brillant, &Prosser, 2008; Ellis, Goodyear, Prosser, & O’Hara, 2006). Researchers found that deep online approaches tend to associate with deepface-to-face approaches (Ellis, Goodyear, Calvo, et al., 2008). Additionally, students who viewed discussions as not only collecting ideas butalso “challenging and improving one’s ideas” or “arriving a more holistic understanding” tended to use deep approaches to online dis-cussions (Ellis et al., 2006). In another example, Chan and Chan (2011) found deep approach positively correlates with online meaningconstruction and online community awareness. Nevertheless, whether approaches to learning have an effect on students’ academic per-formance is still inconclusive. Although deep approaches of learning tended to be associated with better learning outcomes (Cano, 2005;Trigwell & Prosser, 1991), in Bliuc, Ellis, Goodyear, and Piggott’s (2010) study, neither deep approaches to online discussions nor deepapproaches to face-to-face discussions predicted students’ academic performance. In studying students at college level, Ellis et al. (2006)found that students with a cohesive conception and adopted deep approaches tended to have higher term marks. Thus, based on thisobservation, learning approaches in conjunction with other factors can probably better predict students’ academic performance in onlinelearning than learning approaches alone.
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3. Methods
3.1. Research subjects
This study was conducted at a university in the northern part of Taiwan. Data were collected from an “Ecology and Human Society”course for non-life-science majored students (general education course). A total of 116 students participated in the class. As some studentsdid not complete the questionnaires, this study only included 111 research subjects. Most students were sophomore (57%) and 42% ofstudents are female. Fifty seven percent of students were majored in soft discipline. The students were required to express their opinionsabout course-related topics, or reflect on the lectures or face-to-face discussions in the online message board after the class. For validatingthe PAOD questionnaire (research objective 1), additional 204 students in another biology general education course were surveyed. Like thefirst course, both courses implemented online asynchronous discussions extensively.
3.2. Research instruments
The research instruments included two sets of surveys: the Approaches to Learning Biology Questionnaire (ALB) and the Perceptions ofAsynchronous Online Discussion Questionnaire (PAOD). Both sets of questionnaires utilized a five-point Likert scale, with 5 indicatingstrongly agree and 1 indicating strongly disagree. The ALB questionnaire was adopted and revised from the Approaches to Learning ScienceQuestionnaire (ALS, Lee, Johanson, & Tsai, 2008). The ALS questionnaire further divided learning approaches into two subscales: motive andstrategy (Kember, Biggs, & Leung, 2004; Lee et al., 2008). Because the subject area of this general education course is biology, the originalwording of science in the questionnaire was replaced by biology. The ALB questionnaire includes 24 items, consisting of four major con-structs: deep motive (DM), deep strategy (DS), surface motive (SM), and surface strategy (SS). Deep motive refers to students’ intrinsicinterests in learning while surface motive refers to fear of failure in the class. Deep strategy refers to approaches that maximize learningmeanings while surface strategy refers to rote learning or learning with narrow purposes (Lee et al., 2008).
The PAOD questionnaire was created for this study to survey students’ perceptions of four aspects of online discussion: Affective,Cognitive, Skill, and Efficacy aspects. The constructs of the questionnaire were designed based on research findings of online discussions(Finegold & Cooke, 2006; Hew et al., 2010; Lee & Tsai, 2011a) as well as other questionnaires about collaborative learning (e.g., Wen & Tsai,2006). Table 1 provides an illustration of how the design of four surveyed aspects related to constructs or factors in previous studies. TheAffective aspect of online discussion refers to both positive attitude and negative attitude toward online discussions (Finegold & Cooke,2006; Wen & Tsai, 2006). The cognitive aspect refers to students’ perception of the helpfulness and purposes of online discussion forlearning. Despite empirical studies showed improvement of students’ skills after participating in online discussion (e.g., Cathey, 2007; Szabo& Schwartz, 2011), few research instruments, however, were designed to probe students’ perceptions in this regard. Questions related to theSkill aspect thus were designed to understand the extent to which students perceive online asynchronous discussion facilitating their skillsinwriting, reading, critical thinking, and analysis. Finally, the literature showed facilitators’ or peers’ behaviors in online discussion can havesome impact on students’ their own contributions (Finegold & Cooke, 2006; Hew et al., 2010). Therefore, the Efficacy aspect refers tostudents’ satisfaction with both themselves and others’ performance online.
The questionnaire was reviewed by two external experts for content and construct validity. Principle component analysis with varimaxrotationwas conducted as exploratory factor analysis for verifying the validity of the five constructs. The Cronbach’s alpha coefficients werecalculated for measuring the reliability of each construct.
3.3. Online messages
The online discussions were an integral part of the course design and facilitated by teaching assistants of the course. The course wasdesigned with a combination of lectures and face-to-face group activities. After the class, students were required to participate in onlinediscussions in order to receive full credits. The purposes of online discussions were to deepen students’ understanding of the contentcovered in the lectures or to encourage further reflection upon issues raised during face-to-face activities. The online discussions werestarted usually by teaching assistants who posts initial messages to invite students’ responses. For instance, during the discussion of “Theuse of nature resource” one of the teaching assistants posted the following initial message:
Table 1Constructs or empirical findings from past studies that relate to the four aspects of students’ perceptions of online discussion.
Aspects Related constructs or factors
Affection � Positive attitude (Wen & Tsai, 2006)� Enjoyment (Finegold & Cooke, 2006)� Negative attitude (Wen & Tsai, 2006)
Cognition � Usefulness and benefits of online discussion (Finegold & Cooke, 2006)� Perceived purpose of online discussion (Finegold & Cooke, 2006)
Skills � Improvement of critical thinking skills (Szabo, & Schwartz, 2011)� Improvement of writing skills (Cathey, 2007)� Exhibiting surface level thinking or low level knowledge constructiona (Hew et al., 2010)
Efficacy � Self-efficacy (Lee & Tsai, 2011b)� Group member confidence in using the discussion board (Finegold & Cooke, 2006)� Group members not participating or not contributinga (Finegold & Cooke, 2006)� Behavior of instructor or other participants during online discussiona (Hew et al., 2010)
a Indicates factors that limit students’ contribution.
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Dear all:
The following are the websites mentioned by the TA during the class today. You all can try to measure your biology footprints. Pleaseshare the results with the class!
Ecology footprint: http://www.ecofoot.org
CO2 footprint: http://ecolife.epa.gov.tw/Cooler/flash/eco2_main_04.html
Water footprint: http://www.waterfootprint.org/index.php?page¼cal/waterfootprintcalculator_indv
Students can start their own thread of discussion as well. Seven weeks of discussions were included in this study. I did not include thedata from four weeks that students post online as a group since this study focuses on individuals. The first week of discussion was alsoexcluded because students were just warming up.
The online messages from sevenweeks of discussions were coded into three major categories: Initiation, Elaborated Response (ER), andResponse with Resources (RWR). These three categories reflect student-centered discourse. Initiation refers to messages that initiate a newdiscussion thread. In other words, a new topic was proposed, either explicitly or implicitly, and then recognized by others (Hmelo-Silver &Barrows, 2008; Lee & Tsai, 2011a). Other responses are further divided into Elaborated Response and RWR. An elaborated response refersto “statements that include definitions, examples, comparison, judgments, and predictions (Hmelo-Silver & Barrows, 2008, p.63).” RWRs areERs with an explicit reference tomodern publications such as information fromwebsites, books, and news. This special category was devotedto the use of learning resources in order to acknowledge students’ extra efforts of finding and sharing information to support their opinions.Previous research of interactions of online discussions have used similar coding categories such as “reference and authorities (Nor, Hamat, &Embi, 2012)”, or “use of external resources (Kay, 2006)”. By comparison to face-to-face discussions, online asynchronous discussions provideda convenient platform for information sharing and making references to outside resources. From a science education point-of-view, makingreference to external resources can be seen as evidence of critical thinking (Guiller, Dumdell, & Ross, 2008) and seen as part of scientificdiscursive practices (Steinkuehler & Duncan, 2008). Therefore, it is essential to include RWR in the coding categories for this study.
The messages were coded by two independent coders and the two coders reached 86% agreement. A total of 1007 online messages werecoded. We also excluded 12 short responses that only agree or disagree with others without further elaboration or justification. Those shortmessages are at different complexity level compared to ER and RWR (Hmelo-Silver & Barrows, 2008).
3.4. Statistical analysis
Regressional analysis, cluster analysis, and paired-t tests were conducted. For cluster analysis, I first conducted Hierarchical ClusterAnalysis and determined the number of clusters to be three based on theWardMethod’s dendrogram. Then I conducted the K-Mean ClusterAnalysis of three clusters. The differences between the three clusters regarding students’ course final marks and number of messages werethen compared by ANOVA test and post-hoc analysis.
4. Results
4.1. Validation of the PAOD questionnaire
The results of factor analysis showed five factors corresponding to the four aspects in the original design of the questionnaire. The skillaspect was further divided into two constructs – Skill I and Skill II. Skill I refers to reading and writing skills, and Skill II refers to critical
Table 2Validity and reliability of the PAOD questionnaire.
Item Factor 1: Cognition Factor 2: Affection Factor 3: Skill1 Factor 4: Skill2 Factor 5: Efficacy
Factor 1: Cognition a ¼ 0.89Item1 0.764Item2 0.679Item3 0.690Item4 0.858Item5 0.769Factor 2: Affection a ¼ 0.81Item6 0.644Item7 0.748Item8 0.680Item9 0.702Item10 0.676Factor 3: Skill1 a ¼ 0.72Item11 0.699Item12 0.873Factor 4: Skill2 a ¼ 0.82Item13 0.835Item14 0.770Factor 5: Efficacy a ¼ 0.79Item15 0.703Item16 0.856Item17 0.834Total a ¼ 0.90
Table 3Correlations between the number of online messages and students’ perceptions of online discussions.
Scales Cognition Affection Skill I Skill II Efficacy
Initiation 0.02 0.12 0.00 0.06 0.08ER 0.24* 0.35** 0.10 0.22* 0.22*RWR 0.18 0.27** 0.19 0.10 0.21*
**p < 0.01; *p < 0.05.
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thinking and analytical skills. All but two items from the original design was deleted from the original questionnaire because its factorloading was below 0.40. The final version of the questionnaire consists of 17 items accounting for 70.92% of the variance. The numbers ofitems for each construct are shown in Table 2 and all items can be found in Appendix. The Cognition construct includes item 1–5; theAffection construct refer to item 6–10. Item 11 and 12 refer to Skill I and Item 13 and 14 represent Skill II. The efficacy construct consists ofitem 13–17. The reliability of each construct, measured by Cronbach’s alpha, ranges from 0.72 to 0.89. The overall alpha value is 0.90. Theresults showed that the PAOD instrument possesses adequate validity and reliability.
4.2. Correlations
The results show positive correlations between almost all aspects of perceptions of online discussions and the number of online ERs,except for Skill I (see Table 3). Students who posted more ERs online tended to perceive online discussions helpful for learning, perceivepositive emotions toward participating in online discussions, perceive gains of critical and analytical skills through online discussions, bemore satisfied with themselves or others’ performance in online discussions. The number of RWRs is influenced only by students’ perceivedaffection as well the level of perceived efficacy. However, the number of Initiation messages shows no significant correlations to any of theconstructs in the PAOD questionnaire.
In terms of the relationship between students’ online contributions and students’ approaches to learning, the results showed that onlythe number of Initiation messages and RWRs were significantly correlated to some dimensions of learning approaches. The numbers ofInitiationmessages were positively correlated to deep strategies; however, the numbers of Initiationmessages were negatively correlated tosurface strategies (see Table 4). Similarly, the numbers of RWRs were positively correlated to deep motivation and deep strategies; and thenumbers of RWRs were negatively correlated to surface strategies. In other words, students who learned biology with deeper motivationand deeper strategies tended to post more Initiation messages and RWRs. Students who seemed to use surface strategies were less likely toinitiate a new line of discussion and were less likely tomake references to informationwhen responded to others. Students’ levels of surfacemotivation did not relate to students’ online contributions.
4.3. Results of cluster analysis
Cluster 1 represents students who are highly motivated and adopted deep strategies (see Table 5). This group of students, however,scored low in all constructs in the PAOD questionnaire. Cluster 2 represents a group of students who are also highly motivated and whotended to adopt deep strategies. Unlike the first cluster, these students scored highly on both learning approaches and all constructs ofPAOD. The third cluster has the lowest motive (both deep and surface); they were least likely to adopt deep strategies. Their PAOD ratingsland between Cluster 1’s and Cluster 2’s in most constructs.
To further validate the three clusters, ANOVA analyses were conducted to examine the differences of students’ performance, the finalmarks of the course and the three types of messages (see Table 6). Cluster 2 outperformed the other two clusters of students in terms ofcourse marks and the number of ER messages. However, there were no significant differences between Cluster 1 and Cluster 3 in any of thecomparisons. Additionally, the three clusters showed no differences in terms of the number of Initiation messages and RWRs.
5. Discussion and implications
The results of this study are summarized in Table 7. The correlations between students’ perceptions of online discussions or approachesto learning and students contributing messages revealed potential factors that influenced the different types of student contributions. Onthe one hand, student’s approaches to learning seem to be able to impact students’willingness to post Initial messages and RWR. These twokinds of messages may require stronger intrinsic motivation and more efforts than simply replying to others without referring to extrasources of information. Students who were not intrinsically motivated to learn or not willing to adopt deep approaches to learning may beless likely to initiate a new topic for discussions or try to find references for their messages. What also worth noting is that surface strategiesnegatively correlate to Initiation messages and RWRs. This could be related to the fact that most guidelines stress the quantity of messages,disregards the type of messages. The most convenient manner for students to fulfill the course requirements is to reply to others, by
Table 4Correlations between the number of messages and approaches to learning biology.
Scale DM DS SM SS
Initiation 0.19 0.23* 0.14 �0.23*ER 0.13 0.13 �0.09 0.01RWR 0.24* 0.24* 0.11 �0.28*
*p < 0.05.
Table 5Profiles of the three clusters.
Clustering items Cluster 1 Cluster 2 Cluster 3
DM*** 3.39 (0.54) 3.47 (0.80) 2.54 (0.51)DS*** 3.77 (0.58) 3.71 (0.43) 3.00 (0.63)SM*** 2.61 (0.90) 2.99 (0.54) 2.31 (0.63)SS 2.66 (0.50) 2.84 (0.62) 2.96 (0.68)Cognition*** 3.10 (0.59) 4.20 (0.49) 3.63 (0.38)Affection*** 2.53 (0.65) 3.58 (0.57) 3.26 (0.57)Skill1*** 2.96 (0.61) 3.79 (0.56) 3.13 (0.69)Skill2*** 3.35 (0.63) 4.22 (0.52) 3.82 (0.53)Efficacy*** 2.71 (0.54) 3.50 (0.67) 3.29 (0.43)Number of students 24 49 38
***p < 0.001 according to ANOVA tests by comparing the three clusters.
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providing students’ opinions or non-referential information (Finegold & Cooke, 2006). Future posting guidelines can specify the type ofmessages required by the course, depending on the purposes of the course.
On the other hand, students’ perceptions of online discussions seem to be a good predictor of students’ number of ER messages. Thenumber of ER messages positively correlated to the following perceptual aspects – Cognition, Affection, Skill II, and Efficacy. The positivecorrelation between perceived helpfulness of online discussions for learning (i.e., Cognition) and number of ER messages coincided withprevious research in that students’ contributions can be influenced by students’ understanding of learning purposes (Hew et al., 2010). Also,another similarity can be drawn from the findings about the perceived level of Efficacy and the impact of behaviors of other participants inonline discussions (Hew et al., 2010). The Efficacy aspect measured the extent to which students were satisfied by their own performance,peers’ performance, and facilitators’ performance in online discussions. This current study re-confirmed that participants’ perceptions ofothers’ behaviors can be an important factor. The findings of the positive relationships between the perceived level of Affection and numberof onlinemessages echoed Xie and Ke’s (2011) results. Namely, enjoyment predicted ego-centric elaboration interactions. Finally, in terms ofhigher-order cognitive skills, only students’ perceived level of critical thinking skills and analytical skills related to students’ ER posting rate.
Cluster analysis revealed three distinct groups who scored significantly different in the final course marks. This study showed thatstudents who adopted deeper approaches and scored higher in all constructs of perceptions of online discussions outperformed students inthe other two groups. This result seems to support the hypothesis proposed at the beginning of this paper – approaches to learning andperceptions of online discussions together better predicts students’ academic performance. Thus far, not much has been revealed aboutwhat impact students’ academic performance when online discussion is implemented. Past study showed that students with a cohesiveconception and adopted deep approaches tended to have higher term marks (Ellis et al., 2006). Weisskirch and Milburn (2003) foundstudents’ messages addressed specifically to faculty were associated with higher course grades. However, researchers are still far frombuilding a comprehensive model incorporating different variables and learning outcomes. One of major challenges of conducting large-scale, advanced statistical analysis of online discussions is the number of participants. This study is still limited by the number of theparticipants, even though it is a relative large class for implementing online discussions. Future studies should control the interventionfactors and accumulate data from multiple years or from multiple classes.
In conclusion, the implications of this study are tri-folds. First, the results of PAOD questionnaires shed some light on the importance forinstructors to be aware of students’ perceived helpfulness to learning (i.e., cognition and skills), students’ emotions (i.e., affection), and theirperceived efficacy of all participants when participating in online discussions. This study provides a validated questionnaire for future studyof students’ perceptions specific toward online discussions. In terms of teaching practices, making explicitly the value of online discussionfor learning in general or for learning specific skills (e.g., reading or writing skills) can be a method for encouraging contributions. In otherwords, based on the results of this study, the better the students understand that online discussions can help their cognitive activities andskills, the more they are willing to contribute. Making students be aware of what others have contributed (i.e., perceived efficacy of peers)can be another method for promoting engagement.
Second, a unique contribution of this study is classifying online discussions into sub-categories and exploring their relationships to theresults of questionnaires. According to the results in this study, if the instructor wish students to contribute more by responding to eachother, then it is important to develop appropriate perceptions of online discussions. If the instructor wishes students to be more proactive instarting new discussion topics and to find information supporting their opinion, then students need to develop deep strategies and deepmotivation toward the subject area. Also, because the RWR category can be seen as a scientific discursive practice or a critical thinking skill, itseems that students who adopted deep strategies and deepmotivations were likely to show this higher-order skill. This is a research findingthat was not discussed in previous studies regarding learning approaches. Future studies can explore other types of message classificationsystems and examine their causal relationships with other variables.
Table 6Differences between clusters regarding course marks and the number of different types of messages.
Clusters F Post-hoc analysis
1 2 3
Mean (S.D.) Mean (S.D.) Mean (S.D.)
Course mark 86.41 (4.74) 89.68 (3.79) 87.63 (5.33) 4.60* 2 > 1, 2 > 3Initiation 0.43 (0.79) 0.31 (0.68) 0.19 (0.46) 1.05ER 5.22 (4.26) 11.53 (7.89) 6.22 (5.26) 6.66** 2 > 1, 2 > 3RWR 0.30 (0.70) 0.61 (1.68) 0.24 (0.60) 1.09
**p < 0.01; *p < 0.05.
Table 7A summary of results from different analyses in this study.
Outcome of learning Related factors
Initiation message � Deep strategy� Surface strategya
Elaborated response � Perception of cognition� Perception of affection� Perception of critical thinking skills and analytical skills� Perception of efficacy
Response with resources � Perception of affection� Perception of efficacy� Deep motive� Deep strategy� Surface strategya
Course marks � Students who mark higher levels in all five constructs of perceptions of asynchronous online discussion,higher scores in motive (deep and surface), and higher scores in deep strategy
� Higher numbers of ER messagesa Indicates negative correlations.
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Finally, the survey of students’ approaches to learning confirmed that students’ contributions in online discussions relate to their overallmotivation level and learning strategies for the course. This study suggests it is important to look into both factors regarding the specificperceptions toward online discussions and learning approaches toward the subject area. Few studies in the past considered the factors of thesetwo levels together. It is interesting that the result of cluster analysis showed no significant differences between Cluster 1 (higher level oflearning approaches; lower level of perceptions) and Cluster 3 (lower level of learning approaches; higher level of perceptions) either in thenumber of postings or in term marks. It shows that in order for students to perform well in online discussions and in the class in general,students need to adopt deeper learning approaches and develop appropriate perceptions toward the different activities. Especially in blendedenvironments where participating online is not in isolation of other means for learning (e.g., lectures or face-to-face discussions), instructorsshould consider how to enhance students’motivation in general and better incorporate online discussions into other learning experiences inthe course.
Acknowledgment
Special thanks to Dr. Kirk Y. Lin for his support in making this study happen. This study was supported by a research grant from theNational Science Council, Taiwan (under the grant number NSC100-2628-S-018-001 MY3).
Appendix
1. Online discussions help me learn biology2. Online discussions help me understand the relationship between ecology and society3. Online discussions help me better understand the lectures4. Online discussions help clarify some biology concepts5. Online discussions help intergrate biology concepts of knowledge6. Participating in online discussions promote my learning motivation7. I enjoy participating in online discussions8. Online discussions are boring9. Online discussions are time consuming
10. Online discussions are stressful11. Online discussions improve my ability of science reading12. Online discussions improve my ability of science writing13. Online discussions improve my critical thinking skills14. Online discussions improve my analytical skills15. I am satisfied with my own performance in online discussions for this course16. I am satisfied with my classmates’ feedbacks in online discussions for this course17. I am satisfied instructors’ or teaching assistants’ feedbacks in online discussions for this course
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