Recent development in education has resulted to the introduction of computers in collaborative learning (Harasim, 2000). Felix, (2005) asserts that educationists, teachers and learners believe collaborative learning is a new education approach used to improve students’ skills, knowledge and comprehension. Education policy makers agree that computers have created a platform where students share emotions, opinion and behaviors without limits. However, the need to limit the degree of collaboration among students using the computers is required. In this regard, establishing a co-regulation aspect in computer supported collaborated learning (CSCL) is the responsibility of both teachers and students (Salovaara, 2005). Nonetheless, understanding the role of sentiments and how it affects computer supported collaborative learning is a critical issue (Lopez-Morteo & Lopez, 2007). This paper provides a comprehensive review of literature on how sentiments and co-regulation play a critical role in computer supported collaborative learning.
In his article “Emotion, cognition, and behavior”, Dolan (2002), reports that emotions, opinions and behaviors are evidenced through expressions and responses. This research work further indicates that emotions are feelings described through expressions and responses to love, hate, anger and joy. Dolan’s work further argues that when learners exhibit joy and appreciate colleagues, a strong sense of emotion is expressed.
A study by Gunawardena (1995) supports Dolan’s work. Gunawardena (1995) developed a study based on computer supported learning. The author shows how emotions are used for inference purposes in the computer supported collaborative learning. In this regard, research indicates that emotions influence how students perform individual tasks. If modeled to stimulate the participants, emotions influence group performance positively. According to a study by Weiss, Berner, Johnson, Guise, Murphy and Lorenzi (2013), the use of Emotions Awareness Tool (ETA) is instrumental in increasing individual participation. The researchers further assert that emotions play a crucial role in cognitive tuning, which consequently increases social interaction among group members. Sometimes, emotions result to confrontations and differentiation of ideas. In most cases, this differentiation of ideas is expressed through tension, joy, anger and anxiety. On the other hand, increased tension creates emotions that cause a negative impact on the group performance. In such situations, tension from members with competence differences implies the importance of saving the group’s reputation. Nonetheless, differences in emotions can be integral in the development of learners’ efforts. Consequently, learners create a successful learning and interactive environment. According to (Rapisarda, 2002) cohesion, effectiveness and individual learning depend on emotions of the participants in the group
An opinionated group of learners shows signs of diversity (Deering & Shaw, 1997). In this context, group members have different opinions regarding various issues. In most cases, opinions are exhibited through expressions and responses to issues discussed. Criticism, objections, appreciations and problem solving skills are viewed as forms of opinions. Group performance is increased through opinions as individual members appreciate the value of problem-solving skills (Deering & Shaw, 1997). Through this sentiment, opinions are validated and appreciated, therefore, giving members the confidence to contribute in collective tasks. Increased group performance is possible through opinions that promote openness.
Individual and group behavior is a major sentimental aspect that affects the overall performance of collaborative tasks (Ostroff, 1992). Individual behaviors can express either positive or negative sentiments. Positive sentiments imply a sense of satisfaction with the group performance while the opposite is true. In a group task, the diversity of individual behaviors either influences other members either positively or negatively. The need to streamline group behavior toward achieving common goals is a difficult task (Ostroff, 1992). Non-tolerance of the diverse individual behaviors causes conflicts that negatively affect the group performance. Establishing principles and guidelines for the group behavior is a prerequisite to improving performance. Moreover, cohesion through integrate group behavior improves interpersonal relationships and communication. However, harnessing collective behavior and performance depends on the group size.
Researchers have shown that major co-regulation strategies in computer supported collaborative learning (CSCL) include task and team regulations (Tsai, 2013). Task-regulation involves collaborative activities such as discussions of information, regulation of task-related activities, regulation of social activities and social activities (Tsai, 2003). Discussion of information entails sharing of ideas and opinions. In most cases, this information is associated with criticism and frequent questions. Therefore, the inclusion of learners in a social-relational platform facilitated by computers improves the success of a group performance. In addition, regulation of task-related activities is facilitated by assigning group members with individual responsibilities (Tsai, 2013). From this perspective, each member enjoys a sense of independence in executing a task that affects the overall goal of the group. Assigning inquiry tasks to individual members is considered a co-regulatory approach. In most cases, inquiry tasks involve open-ended and structured questions for each group member. Task regulations are cognitive initiatives aimed at orienting learners to an activity, as well as helping the student to monitor and evaluate the same.
Team regulation is a co-regulation strategy that aims at improving learners’ coordination, and helps students in planning and monitoring the group activities (Hadwin, Jarvela & Miller, 2011). Team-regulation is integral in planning the group’s social activities. From this perspective, social activities require coordination and monitoring to improve sharing of information and communication between members. In this regard, positive outcome and performance of the group depends on how well social parameters are coordinated (Hadwin, Jarvela & Miller, 2011). Both task and team regulation improves group performance from an individual and team’s perspectives.
As indicated earlier, major forms of sentiments in CSCL include emotions, opinions and behaviors. In this context, relationships between the sentiments and co-regulation strategies such as task and team regulation are evidenced differently. Emotions play a crucial part in establishing the success of co-regulation strategies used in CSCL (Jarvela, 2011). For example, task regulations require individuals with a positive attitude and confident. Moreover, the behavior of individual learners affects the success of a collaborated effort based on their behaviors. As indicated earlier, task regulation requires a form of cognitive tuning that provokes creativity among individual learners. Individual learners who exhibit negative emotions are likely to make co-regulation strategies such as task and social activity regulations ineffective (Jarvela, 2011). Opinions are a major factor in determining whether collaborative learning provokes critical thinking and exhibits problem solving skills (Bean, 2011). It is the objective of computer supported collaborative learning to ensure learners acquire skills and knowledge that can be used to solve real-life problems (Bernard & Rubalcava, 2000). From this perspective, opinions can be used to generate ideas on effective co-regulation strategies that make computer supported collaborative learning effective. In addition, behaviors determine whether the task, team or social activities co-regulation strategies will be effective (Bernard & Rubalcava, 2000). Behavior of the individual student determines whether delivering on the designed assignments will contribute effectively to the overall group work. Apparently, individual students assume individual tasks to exhibit personal competency (Bernard & Rubalcava, 2000). Group behavior sentiments have a direct impact on co-regulation strategies such as team and social regulations. Team regulations especially on group activities depend on cognitive and behavioral elements of the participants. The formation of a group behavior acts as a guideline to achieving CSCL objectives through task, team and social co-regulation activities. Therefore, behavior determines the success of co-regulation strategies in relation to underlying CSCL objectives.
In order, to understand the causal relationship among the elements affecting CSCL, it is crucial to categorize sentiments, co-regulation and group performance as variables. Therefore, a change in one of the variables affects the other, and ultimately the objective of CSCL. However, the primary goals of CSCL must remain constant, in order, to determine the relationship among the variables (Feidakiss, Daradoumis, Caballe & Conesa, 2012).
As indicated earlier, sentiments identified in CSCL include emotions, opinions and behaviors (Feidakiss, Daradoumis, Caballe & Conesa, 2012). In any case, emotions, opinions and behaviors change regularly among learners. A change in emotions determines the co-regulation strategy to be used in computer supported collaborative learning process (Feidakiss, Daradoumis, Caballe & Conesa, 2012). A negative change in emotions renders co-regulation activities ineffective, and ultimately this affects group performance (Denham & Burton, 2003). On the contrary, a positive behavior is likely to influence the execution of co-regulation strategies, therefore, improving the group performance. For example, improvement on individual confidence and attitude makes the task, team and social co-regulation strategies successful. In this context, the overall group performance in implementing CSCL initiatives is successful.
Encouraging sharing of opinion among the group participants is a critical issue. Through opinions, ideas are shared and implemented. Therefore, co-regulation activities are characterized with critical thinking and problem solving skills (Shea & Bidjerano, 2010). However, effective opinions are derived from emotions that support the CSCL objective. Lack of increased tension as a result of opinionated participants facilitates the execution of co-regulation activities, and ultimately, improves the group performance (Shea & Bidjerano, 2010). On the other hand, low participation rate from group members in terms of opinions and ideas reduces the team performance.
A comparison between high and low performance groups shows the significance of sentiments and co-regulation strategies in CSCL.
A high performance group exhibits positive sentiments (Gressick & Derry, 2010). In this context, high performers are known to exhibit emotions characterized with confidence and good attitude. For example, expression of understanding and appreciation among high performing group members is evidenced (Gressick & Derry, 2010). High performers express satisfaction in various undertakings and respond to criticism with appreciation. In fact, high performers use criticism to improve on individual and group performance (Gressick & Derry, 2010). Sharing of happiness and joy is a common feature among high achievers. However, sharing of ideas emanates from satisfaction and the policy of teamwork.
On the other hand, low performers exhibit negative emotions such as sadness, anger and dissatisfaction (Solimeno, Mebane, Tomai & Francescato, 2008). In most cases, such emotions emanate from conflicts between members of the group. In this context, low performers do not perceive the value of sharing ideas, and using social platforms to create bonds with fellow students. Lack of motivation among low performers makes students dull and uninspiring (Solimeno, Mebane, Tomai & Francescato, 2008).
High performers are opinionated individuals and value their ideas (Gressick & Derry, 2010). In this context, opinions are shared among high performers, as well as criticism of the same. High performers understand the value of opinion in critical thinking and developing problem solving skills (Gressick & Derry, 2010). From this perspective, improving on individual and group performance is evidenced. On the other hand, low performers disregard an opinion and perceive such as hindrances to completion of individual or group activities. In most cases, low performers develop a low opinion about opinion and disregard criticism.
High performers prefer co-regulation activities associated with cognitive tuning. From this perspective, high performers prefer using task regulation activities that are challenging and provoke critical thinking (Gressick & Derry, 2010). However, high performers include self-regulation as part of the co-regulation strategy. In this context, using an instructor, a colleague or teacher to co-regulate on activities is considered an effective means to improving performance. In addition, utilizing the help of a teacher and colleagues to co-regulate activities under group assignments is considered effective. Moreover, regulations for social activities are core initiatives in co-regulating various class-based and co-curricular activities. On the contrary, low performers highly depend on instructors for co-regulation (Solimeno, Mebane, Tomai & Francescato, 2008). From this perceptive, lack of creativity and mistrust among colleagues are a constant feature in low performers. Moreover, this explains why social activities are not a favorite of low performers (Solimeno, Mebane, Tomai & Francescato, 2008). In fact, low performers find it difficult following instructions especially from colleagues.
Bean, J. C. (2011). Engaging ideas: The professor’s guide to integrating writing, critical thinking, and active learning in the classroom. Hoboken, NJ: John Wiley & Sons.
Bernard, R. M., & Rubalcava, B. R. D. (2000). Collaborative online distance learning: Issues for future practice and research. Distance Education, 21(2), 260-277.
Deering, C. G., & Shaw, S. J. (1997). Dealing with difficult students in the classroom. Nurse educator, 22(5), 19-23.
Denham, S. A., & Burton, R. (2003). Social and emotional prevention and intervention programming for preschoolers. New York, NY: Kluwer Academic/Plenum.
Dolan, R. J. (2002). Emotion, cognition, and behavior. Science, 298(5596), 1191-1194.
Feidakis, M., Daradoumis, T., Caballé, S., & Conesa, J. (2012). Design of an emotion aware e–learning system. International Journal of Knowledge and Learning, 8(3), 219-238.
Felix, U. (2005). E-learning pedagogy in the third millennium: the need for combining social and cognitive constructivist approaches. ReCALL, 17(1), 85-100.
Gressick, J., & Derry, S. J. (2010). Distributed leadership in online groups. International Journal of Computer-Supported Collaborative Learning, 5(2), 211-236.
Gunawardena, C. N. (1995). Social presence theory and implications for interaction and collaborative learning in computer conferences. International journal of educational telecommunications, 1(2), 147-166.
Hadwin, A. F., Jarvela, S., & Miller, M. (2011). Self-regulated, co-regulated, and socially shared regulation of learning. Handbook of self-regulation of learning and performance, 65-84.
Harasim, L. (2000). Shift happens: Online education as a new paradigm in learning. The Internet and higher education, 3(1), 41-61.
Jarvela, S. (2011). Socioemotional Aspects of Technology-Supported Learning. Social and Emotional Aspects of Learning, 263.
Lopez-Morteo, G., & López, G. (2007). Computer support for learning mathematics: A learning environment based on recreational learning objects. Computers & Education, 48(4), 618-641.
Ostroff, C. (1992). The relationship between satisfaction, attitudes, and performance: An organizational level analysis. Journal of applied psychology, 77(6), 963.
Rapisarda, B. A. (2002). The impact of emotional intelligence on work team cohesiveness and performance. International Journal of Organizational Analysis, 10(4), 363-379.
Salovaara, H. (2005). An exploration of students’ strategy use in inquiry‐based computer‐supported collaborative learning. Journal of Computer Assisted Learning, 21(1), 39-52.
Shea, P., & Bidjerano, T. (2010). Learning presence: Towards a theory of self-efficacy, self-regulation, and the development of a communities of inquiry in online and blended learning environments. Computers & Education, 55(4), 1721-1731.
Solimeno, A., Mebane, M. E., Tomai, M., & Francescato, D. (2008). The influence of students and teachers characteristics on the efficacy of face-to-face and computer supported collaborative learning. Computers & Education, 51(1), 109-128.
Tsai, C. W. (2013). Applying web-based co-regulated learning to develop students’ learning and involvement in a blended computing course. Interactive Learning Environments, 1-12.
Weiss, J. B., Berner, E. S., Johnson, K. B., Giuse, D. A., Murphy, B. A., & Lorenzi, N. M. (2013). Recommendations for the design, implementation and evaluation of social support in online communities, networks, and groups. Journal of biomedical informatics, 46(6), 970-976.