Maximizing learning from collaborative activities

Maximizing learning from collaborative activities Rachel J. Lam (rachel.lam@asu.edu) Arizona State University Teachers College, 300 E. Lemon St. Tempe, AZ 85281 Abstract Utilizing a Preparation for Future Learning paradigm and the Interactive-Constructive-Active-Passive framework, this study examined how two different kinds of cognitively engaging activities prepared students to learn from collaborating. Findings show that preparing prior to collaborating improved learning, but a difference was not detected in the type of preparation. In addition, differences in learning outcomes were only present in measures of deep knowledge. Analyses used a multilevel method targeted to dyadic data. Discussion addresses designing collaborative classroom activities that are effective and efficient for deep learning, as well as the importance of aligning assessments to depth of learning. Keywords: collaborative learning; preparation for future learning; cognitive engagement; classroom learning. Introduction Collaborative learning has become a common instructional strategy in a variety of educational settings because of its potential to boost student learning. Through peer discussion, students can receive immediate feedback, ask questions, generate explanations, challenge each other, jointly construct understanding, and elaborate on each other’s ideas, which are all behaviors that have been shown to improve learning outcomes in both the classroom and laboratory. However, despite the extensive research that has been conducted on collaborative learning, the literature is still unclear as to what factors lead to the best learning outcomes, in particular, for deep understanding of concepts. Thus, this work aimed to investigate two factors that may improve deep knowledge, in particular, in a conceptual (as opposed to a problem-solving) domain: (a) individually engaging in the learning material prior to collaborating and (b) “constructively” engaging, where students are generating (constructing) new knowledge beyond the learning material. There are mixed results as to how collaboration affects student learning (Barron, 2003; Craig, Chi, & VanLehn, 2009). In general, students do not always take advantage of the benefits collaboration affords, thus, researchers have searched for ways to help students collaborate more effectively. Methods such as training students in collaboration skills (Hausmann, 2006; Uesaka & Manalo, 2011), providing structured guidance to students while interacting (Coleman, 1998; Walker, Rummel, & Koedinger, 2011), and designing collaborative learning environments that elicit meaningful discussion (Engle & Conant, 2002; Kapur & Bielaczyc, 2012) have been found to improve learning from collaborating. However, there are also challenges and limitations to these methods. One limitation to training students in specific skills before collaborating is that they often fail to retain those skills after time (Webb, Nemer, & Ing, 2006). The challenge of structured guidance during collaboration is that too much can constrain creativity and flexible discussion, which can hinder learning (Cohen, 1994). Therefore, one question that remains is, does the effort and time that it takes to train or guide students in collaborative behaviors really pay off? Work that has investigated the design of collaborative activities to naturally elicit effective dialoguing addresses this challenge, showing that open-ended and flexible tasks can enrich discussion (Janssen, Erkens, Kirshner, & Kanselaar, 2010; Van Boxtel, Van der Linden, & Kanselaar, 2000). However, this only occurs when students have sufficient prior knowledge (Nokes-Malach, Meade, & Morrow, 2012). Thus, a collaborative learning method that avoids the time and effort needed to train students in particular skills or structure their instance-by-instance dialogic behaviors, while providing the opportunities for students to acquire adequate prior knowledge is investigated in the current study. Cognitive theoretical models Two cognitive theoretical models supported the design of the collaborative activities in this study. The Interactive- Constructive-Active-Passive (ICAP) framework and the Preparation for Future Learning (PFL) paradigm are described below. The ICAP framework The ICAP framework differentiates student engagement in learning tasks by categorizing students’ overt behaviors as Interactive, Constructive, Active, or Passive, and is founded on theoretical assumptions about how those behaviors link to different cognitive processes (Chi, 2009; Menekse, Stump, Krause, & Chi, 2012). An Interactive behavior might be debating or extending a partner’s idea and the cognitive process underlying Interactive engagement would be co-creating knowledge. Inventing a rule, self-explaining, or creating a concept map would be Constructive, the underlying cognitive process being creating new knowledge. Active behaviors include highlighting a textbook chapter or copying solutions steps from the board, and correspond to assimilating knowledge. Listening or watching would be considered Passive, corresponding to the process of storing knowledge. The ICAP hypothesis makes the prediction that Interactive activities will produce better learning outcomes than Constructive activities, which are better than Active