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Group Formation for Small-Group Learning: Are Heterogeneous Groups More Productive?
Abstract
There is an underexploited potential in enhancing massive online learning courses through small-group learning activities. Size and diversity allow for optimizing group composition in small-group tasks. The purpose of this paper was to investigate how groups formed based on learner behavior affect productivity of students in a small-group task. Students classified as high, average and low were randomly assigned to homogeneous or heterogeneous groups. Results indicate that overall, heterogeneous groups were either similarly or a bit more productive than homogeneous groups. Yet, we found that homogeneous groups classified as high-level were as or more than heterogeneous groups. However, heterogeneous groups were still more productive than homogeneous-average and homogeneous-low groups suggesting heterogeneous groups are the best choice for the entire community. Students classified as low-level were more productive in homogeneous groups, suggesting that grouping less active students together, makes social loafing more difficult and students participate more.
... These criteria have been used in a wide variety of studies that can be found in the literature. These studies usually consider factors such as the students' learning style [6,7], their thinking style [8], their knowledge and behaviour [9,10], or characteristics such as their gender, skills, and personality [11][12][13][14][15][16], among others. ...
... Below are some o portant related works of recent years, including a brief description of their Battur et al. [16] propose in their study that teams be formed based o complementary skills and ensuring that each team has an expert member fied skill. Wichmann et al. [9] investigated how group formation based on iour affects productivity on a small group task. Amara et al. [42] carried form homogeneous groups in a mobile collaborative learning environment ing attributes personalized selection; the technique used was the K-Me Sadeghi and Kardan [13] and Amarasinghe et al. [12] propose binary inte gramming based on task assignment, gender, and language preferences mation optimization technique. ...
... Battur et al. [16] propose in their study that teams be formed based on the student's complementary skills and ensuring that each team has an expert member in each identified skill. Wichmann et al. [9] investigated how group formation based on student behaviour affects productivity on a small group task. Amara et al. [42] carried out research to form homogeneous groups in a mobile collaborative learning environment, with a grouping attributes personalized selection; the technique used was the K-Means algorithm. ...
In this paper, an approach based on genetic algorithms is proposed to form groups in collaborative learning scenarios, considering the students’ personality traits as a criterion for grouping. This formation is carried out in two stages: In the first, the information of the students is collected from a psychometric instrument based on the Big Five personality model; whereas, in the second, this information feeds a genetic algorithm that is in charge of performing the grouping iteratively, seeking for an optimal formation. The results presented here correspond to the functional and empirical validation of the approach. It is found that the described methodology is useful to obtain groups with the desired characteristics. The specific objective is to provide a strategy that makes it possible to subsequently assess in the context what type of approach (homogeneous, heterogeneous, or mixed) is the most appropriate to organize the groups.
... Most experts agreed that the number of members in a group is at least three with small groups arbitrarily ranging from 3 to 15 (Tubbs, 2012). However, Lencioni (2007) suggests the number of members per group ranges between 3 and 12. So, there is a call for studying the consequence of small group composition on the nature of group tasks (Wichmann et al., 2016). ...
... Heterogeneous ability grouping is more fruitful than homogeneous ability grouping for learners with advanced understanding, while on average, with poor ability learners who have difficulty in learning (Murphy et al., 2017). Heterogeneous groups are still more beneficial than homogeneous groups implying heterogeneous groups are the better option in group composition (Wichmann et al., 2016). Moreover, heterogeneous groups of students with a diversity of thinking preferences can acquire learning from sharing ideas and happy with the learning experiences and the results of their work (Sundquist, 2019). ...
... It is suggested to have a small group task for the students to facilitate learning in groups. Small-group tasks give a better avenue for students to identify and settle knowledge deficiency and cognitive struggle (Wichmann et al., 2016). ...
... Due to an increased recent interest in applying CL in MOOCs, several approaches towards group formation have been proposed (Sinha 2014;Spoelstra, Van Rosmalen, and Sloep 2014;Wen 2016;Wichmann et al. 2016;Cheng et al. 2017;Zheng 2017). However, these previous research efforts have not taken into account the existence of some learner subpopulations in MOOCs with a very low engagement level (e.g. ...
... Finally, Wichmann et al. (2016) compared the performance of groups formed heterogeneously and homogeneously based on students' forum engagement. Their results showed that, overall, heterogeneous groups were either similarly or a bit more productive than homogeneous groups. ...
Collaborative learning can improve the pedagogical effectiveness of MOOCs. Group formation, an essential step in the design of collaborative learning activities, can be challenging in MOOCs given the scale and the wide variety in such contexts. We discuss the need for considering the behaviours of the students in the course to form groups in MOOC contexts, and propose a grouping approach that employs homogeneity in terms of students’ engagement in the course. Two grouping strategies with different degrees of homogeneity are derived from this approach, and their impact to form successful groups is examined in a real MOOC context. The grouping criteria were established using student activity logs (e.g. page-views). The role of the timing of grouping was also examined by carrying out the intervention once in the first and once in the second half of the course. The results indicate that in both interventions, the groups formed with a greater degree of homogeneity had higher rates of task-completion and peer interactions, Additionally, students from these groups reported higher levels of satisfaction with their group experiences. On the other hand, a consistent improvement of all indicators was observed in the second intervention, since student engagement becomes more stable later in the course.
... Recent studies explore the link between group structure, social networks, and learning outcomes (Gašević, Zouaq, & Janzen, 2013;Candia, Oyarzún, et al., 2022;Candia, Pulgar, & Pinheiro, 2022). Heterogeneous groups, with diverse skills and backgrounds, often foster robust interactions and idea exchange (Wichmann et al., 2016;Brown & Paulus, 2002). In contrast, homogeneous groups may limit diverse viewpoints, narrowing the scope of learning (Larson, 2007). ...
... Similar to this, Murphy, Greene, Firetto, Li, Lobczowski, Duke, & Croninger (2017) grouped learners based on ability and found that heterogeneous was more beneficial than the homogeneous group. Further, the superiority of heterogeneous grouping can be observed in Massive Open Online Courses (MOOCs), in which Wichmann, Hecking, Elson, Christmann, Herrmann, Hoppe (2016) revealed that heterogeneous groups had trends of slightly more productivity than homogeneous in a number of areas. ...
Cooperative learning has been proved as one of the most used instructional approaches that maximizes student use of time and resources. Cooperative learning ensures that learners work in groups and discuss materials that have been organized by their teachers. Using this method, learners are said to academically progress than by using most methods of learning including individual learning. There has been a strong debate however, as to which grouping system ensures higher academic progress between homogeneous and heterogeneous method. This study aimed to find out if participation in cooperative learning led to improved academic performance in learners. Further, it aimed to find out if the student performance was different between homogeneously grouped learners and heterogeneously grouped learners. The study employed educational data mining methods to mine data for correlation and clustering the learners into groups. Results indicate a statistically significant higher academic performance in learners in the homogeneous group than those in the heterogeneous group.
... For example, previous studies have investigated the differences in students' academic achievement during collaborative and cooperative learning by comparing homogeneous with heterogeneous ability groups [50]. Other studies also investigated the differences in students' learning gains from collaboration between homogeneous and heterogeneous interactions based on skill diversity, gender [4], productivity [49], and personality [39]. The association between social capital (operationalised as network centrality) and academic performance was also found to be moderated by homophily [22]. ...
... The second rank activity can stimulate students who have never spoken to speak their ideas and suggestions, especially during the presentation session. This finding consistent with Wichmann et al. [36] that indicates the productivity in homogeneous groups, the students who were less active contributed more when being grouped together. The third factor indicates that it is good as students will be exposed to a wide range of information on the topics provided. ...
Student-centered learning (SCL) is one of the teaching methods commonly used nowadays as it encourages the active participation and engagement of students in the classroom, especially for the engineering theoretical subject. This study is aimed to examine the factors of students’ involvement and participation towards the SCL Teaching Method in terms of the activities, benefits, problems, and limitations of student involvement. The quantitative data are obtained from the responses of students that enroll in an engineering theoretical subject in the Universiti Teknologi MARA (UiTM) Pahang Civil Engineering Diploma Program. These questionnaires were being classified into five major factors that are the formation of group studies for SCL activities, activities conducted for SCL teaching method, benefits that they gain from SCL method, problems that they encounter during SCL, and suggestions for student improvement towards the activity SCL session. The collected data were analyzed quantitatively by using the percentage and mean method in SPSS computer software. The Relative Importance Index (RII) system was used to quantify the relative importance of involvement factors. This study revealed three main factors affecting the participation and engagement of students in the classroom. This study has an important contribution to help academicians to improve and enhance their teaching method to achieve the objective of the SCL method in the future.
... Task completion, satisfaction, peer interaction and overall experiences were better in groups with a higher degree of homogeneity. In a similar attempt, Wichmann et al. (2016, august) found heterogeneous groups slightly more productive than homogeneous groups in a massive online learning course. ...
The study aims to investigate the effect of homogenous and heterogeneous grouping on students’ achievement and experiences of learning in a collaborative environment. A collaborative learning environment has been used as a pedagogical tool for a long time now. However, there is no clarity on which grouping strategy to use. In this paper, we study the impact of grouping on students’ performances. We aim to examine how different grouping arrangements, leading to different learning environments, affect students’ academic achievement. Also, in most cases homogeneity or heterogeneity is decided on the basis of students’ ability. For group learning, students were grouped into two different settings on the basis of their learning perspectives derived from class notes and their personality types. In the present study, we used a novel algorithm based on k modes clustering. Grouping indeed improved students’ performance, particularly, the heterogeneous groups performed better than the homogenous groups. Students’ experience with learning in the two different environments indicates that they were more satisfied with homogeneous group settings.
... Furthermore, certain group compositions may be more optimal and/or just than others. There is a long debate in the educational sciences regarding ability-grouping, for example, which has centered around the question of whether homogeneous or heterogeneous learning groups are most beneficial for learning (e.g., Steenbergen-Hu 2016; see also Wichmann et al., 2016). Empirical questions regarding diversity in groups (what kinds of groups bring about the best utility) are accompanied by ethical onese.g., should groups be diverse along certain lines even if this results in costs in terms of utility or productivity? ...
Algorithmic group formation has become a flourishing research area in the computer sciences, and more recently in the field of data mining and fair machine learning. Application domains for algorithmic solutions to grouping span wide, from team-recommendation and formation in work settings to ability-grouping in education. Recent work has also focused on fairness in group-formation. We briefly review literature on algorithmic team-formation and consider fairness in different group-formation contexts. We articulate different dimensions and constraints that are relevant for fair group-formation and discuss the tension between utility and fairness. Many problems and limitations regarding formal definitions of fairness explicated in the fair machine learning literature apply also in the context of group-formation. We suggest some limits to the relevance of fairness in general and algorithmic fairness, in particular. We argue that algorithmic fairness is less relevant to some groups because of the way they come to existence or because fairness is not a central value for them. Other central values are subjective rights; autonomy or liberty; legitimacy and authority; solidarity; and diversity, each of which can be in tension with optimal fairness-and-utility. But within acceptable limits, we argue that fairness is indeed a valuable goal that may be in tension with maximization of the relevant types of utility.
... Battur et al. [33] propose in their study that the teams be formed based on the student's complementary skills and ensuring that each team has an expert member in each identified skill. Wichmann et al. [34] investigated how group formation based on student behavior affects productivity in a small group task. Amara et al. [35] conducted research to form homogeneous groups in a mobile collaborative learning environment, with a personalized selection of training attributes; the technique used was the K-Means algorithm. ...
Considering that group formation is key when developing activities in collaborative learning scenarios, this paper aims to propose a strategy based on a genetic algorithm approach for achieving optimal collaborative learning groups, considering the students’ personality traits as grouping criteria. A controlled experiment was designed with 238 students, quantifying their personality traits through the “big five inventory” (BFI), forming working groups and developing a collaborative activity in programming and related courses. The experiment results allowed validation, not only from a computational point of view evaluating the algorithm performance but also from a pedagogical point of view, confronting the results obtained by students applying the proposed approach with those obtained through other group formation strategies. The highlight of the study is that those groups whose formation was pre-established by the teachers through the proposed strategy have generally had a better collaborative performance than the groups with traditional formation, except in the case of heterogeneous formation, at the time of developing a collaborative activity. In addition, through the experiment, it was found that not considering criteria related to personality traits before the group formation generally led to lower results.
... However, forming homogeneous groups can also be beneficial. For instance, Wichmann et al. (2016) found that grouping less active students with other less active students increased their participation. Therefore, when assigning students to groups, it is worth considering how student characteristics might influence interaction processes. ...
Purpose
Against the background of empirical research on computer-supported collaborative learning (CSCL), the purpose of this paper is to advocate implementing collaborative learning activities into online distance education courses to engage learners in interactive knowledge construction. This study uses empirical evidence to illustrate how educators can integrate collaborative learning and designated collaboration support into their instructional design.
Design/methodology/approach
This study presents a general review of research literature from the field of CSCL to highlight productive interaction between learners as key learning mechanisms, summarize core features of collaborative tasks, which promote interaction between learners and present group awareness tools and collaboration scripts as two complementary approaches to support groups during collaborative learning.
Findings
Empirical research suggests that collaborative learning is an effective learning activity and that incorporating collaborative learning into online courses benefits learners in terms of learning and social aspects such as social presence. However, to leverage the potential of collaborative learning, careful instructional design that promotes productive interaction between students is necessary.
Originality/value
This paper provides an overview on the topic of collaborative learning and how meaningful interaction between learners can be fostered. Specifically, this study details how collaborative tasks can be designed and how collaboration support can be used to provide students with opportunities for interaction that fosters acquiring new domain-specific knowledge as well as collaboration skills. To allow educators to design and incorporate collaborative learning activities into their own online teaching, the authors provide a theoretical basis for understanding the mechanisms behind effective collaborative learning as well as examples and practical considerations.
... Heterogeneous grouping distributes students among groups in a way that the members of each group to have different characteristics. This diversity can be beneficial to improve learning outcomes, as group potential can be increased through the different attributes of members [23], [24]. For instance, grouping students with different misconceptions may help the successful project delivery than having students with the same misconceptions in a group, or grouping students all of who are introverted may cause project failure. ...
Social Networking-based Learning (SN-Learning) is one of the most promising innovations to promote learning via a social network, and thus, providing a more interactive, student-centered, cooperative and on demand environment. In such an environment, group formation plays an important role to the effectiveness of learning process. Adequate groups foster student interactions and increase learning outcomes. However, group formation is a complex task and requires automatic approaches to produce the optimal results in short time. To this direction, this paper presents a novel genetic algorithm for student grouping in a SN-Learning system. Its innovations pertain to the attributes used for the composition of groups and genetic operators applied. In particular, student attributes refer to the three main dimensions of learning in a SN-Learning environment: academic, cognitive, and social. Regarding genetic operators, the algorithm performs two crossover operators: a modification of 2-point crossover and a new approach, called 1-point per group crossover. Evaluating the proposed algorithm performance, the results show that it is more efficient than simple genetic algorithm approach, and considers a larger number of parameters than usual. Moreover, from pedagogical perspective, a positive students' attitude and high acceptance towards our group formation method is indicated.
... Papers with an overall quality assessment score greater than 5 were included. At the end of this stage, a list of 21 papers were selected: S01 [3], S02 [5], S03 [6], S04 [9], S05 [21], S06 [22], S07 [26], S08 [30], S09 [32], S10 [35], S11 [38],S12 [39], S13 [1], S14 [2], S15 [4], S16[13] S17 [15], S18 [23], S19 [24], S20 [27], S21 [31]. ...
This paper presents a systematic literature review (SLR) that investigates group formation, as a first step towards automated group formation for collaborative learning. Out of 105 papers selected for review, after using specific selection and a quality assessment method, a final list of 21 relevant studies was selected for analysis. The review revealed the current state of the art in group formation.
... This can also be seen from the standard deviation of the wordcount reported in Table 1. Hence the wordcount is still a distinguishing variable as also reported in previous analyses [7,19]. ...
Establishing small learning groups in online courses is a possible way to foster collaborative knowledge building in an engaging and effective learning community. To enable group activities it is not enough to design collaborative tasks and to provide collaboration tools for online scenarios. Collaboration in such learning groups is prone to fail or even not to be initiated without explicit guidance. In the target situations, interventions and guiding mechanisms have to scale with a growing number of course participants. To achieve this under privacy constraints, we aim at identifying target indicators for well-functioning group work that do not rely on any kind of information about individual learners.
Kooperatives Lernen in Kleingruppen im Schul- und Hochschulkontext hat sich als lernförderliches Arrangement herausgestellt. Gleichzeitig zeigt die Forschung jedoch auch, dass kooperatives Lernen kein Selbstläufer ist und dass zusätzliche pädagogische Unterstützung Lernenden dabei helfen kann, die Potentiale der Kooperation mit anderen für das Lernen zu nutzen. Im Rahmen dieses Beitrags fokussieren wir auf Computerunterstützung für kooperatives Lernen (computer-supported collaborative learning, CSCL) und zeigen auf, welche Rolle digitale Technik bei der Unterstützung von kooperativem Lernen einnehmen kann. Anhand eines kurzen historischen Überblicks skizzieren wir, wie sich Computerunterstützung zuerst für individuelles Lernen und anschließend für kooperatives Lernen entwickelt hat. Unter Rückgriff auf das Konzept der „Affordanzen“ stellen wir dar, welche Rollen digitale Technik bei der Unterstützung kooperativen Lernens einnehmen kann. Abschließend diskutieren wir die Herausforderungen und Stärken digitaler Technik bei der Unterstützung kooperativen Lernens entlang von Fragen nach dem Zugang zu digitaler Unterstützung, ihrer Skalierbarkeit, ihrer Konfiguration, sowie der Frage nach einer Handlungsautonomie für die Lernenden.
This article presents a multi‐part initiative to support diverse learners by building class community and peer networks in an introductory statistics course. This was accomplished through multiple techniques, such as implementing icebreaker questions and using randomly assigned student working groups. The Socrative online software utilized regularly by instructors allowed students to be randomized into groups to collaboratively answer questions provided by the instructors. A multi‐part group project was also administered where students worked cooperatively to analyze swim race data from the 2016 Olympics. Students completed a pre‐semester survey in which they provided feedback on previous mathematics or statistics courses related to the level of course engagement, the benefit of group learning, and their ability to discuss course content during class. A post‐semester survey was administered to answer similar questions regarding the statistics course they were currently taking.
Collaborative learning is a useful teaching method in high education and is used in various study areas in the classroom or online. For effective collaboration, group formation is one of the critical factors. It often requires preparation by a teacher. In this study, to help a teacher selection grouping, we propose the models for group formation based on the principles: intra- or inter-group homogeneity or heterogeneity. It depends on the situations what principle is suitable and preferable. We use students’ profiles by their inner evaluation to measure the group status, suitable for representing their tendencies. The inner evaluation is denoted as a normalized interval vector of criteria where an interval element indicates a possible evaluation of a criterion. The first and second models are from the intra-group viewpoint by maximizing or minimizing the difference or similarity of members in each group. The students of similar or different tendencies are assigned to the same group. The third model is from the inter-group viewpoint by minimizing differences among groups concerning group tendency. Therefore, balanced groups are obtained.
Groupwork is a useful classroom tool, but may not fully benefit students who are ostracized by peers due to nonacademic factors, like weight. Here, undergraduate students estimated GPAs of hypothetical student peers who were described as “overweight” or “normal-weight” and judged how much they wanted to work with them. We found small but significant effects: students rated overweight peers as having lower GPAs and reported less desire to work with them relative to normal-weight peers. Instructors should be cognizant that overweight students are believed by their peers to be less desirable class partners and may be excluded from classroom participation.
Group discussions can be beneficial to keep students engaged during online learning provided that group members form a good match. For instance, the success of the group depends to a great extent on the engagement of its group members. During the COVID-19 pandemic, it became possible to automatically detect engagement using students learning data as all the teaching and learning processes can be recorded. This paper presents an exploratory study that compares the grouping of students with homogeneous engagement levels together to the grouping of students with heterogeneous engagement levels together. We measured the student engagement using their activity logs in an e-book system. We conducted a study with 23 students enrolled in an online class and analyzed the impact of different grouping styles on the learning achievement and student satisfaction of low, mid, and high engagement students. The results show that grouping students with homogeneous engagement levels together is associated with a significant increase in the learning achievement of low-engagement students and the satisfaction of high-engagement students.
The incorporation of collaborative work in the educational field grows day after day, as does the research associated with this topic. One of the most recurring problems faced by teachers who want to employ this learning strategy is the good students group formation since this task can be complex both conceptually and computationally, especially if the aim is to automate it. Considering that group formation is key when developing activities in collaborative learning scenarios, this paper aims to propose a technique based on a genetic algorithm approach for achieving homogeneous groups, considering the students personality traits as grouping criteria, since, the aforementioned traits, have proven to be significantly predictive of academic performance and, in addition, to be associated with variables with a strong influence on academic success, such as intelligence and beliefs of self-efficacy. A controlled experiment was designed with 132 students, quantifying the personality traits through the Big Five InventoryBFI, forming working groups and developing a collaborative activity in the initial programming courses. The experiment results allowed validation, not only from a computational point of view evaluating the algorithm performance but also from a pedagogical point of view, confronting the results obtained by students: formation based on personality traits versus formation by the students preference. The highlight of the study is that the homogeneous groups generated by the proposed approach produced better academic results compared to those obtained by groups generated by the students' preference, at the moment of developing a collaborative activity.
Collaborative learning in small groups can enrich and enhance the learning experience in large online courses by facilitating interaction and collaborative knowledge building between peers. on the work reported here addresses scenarios based on asynchronous communication and exchange. As compared to synchronous and face-to-face settings, these scenarios require higher explicit coordination efforts and specific support mechanisms to reach out to inactive group members. This can be achieved by human tutors as well as through automatic interventions by the system in the organization and facilitation of group work. Our approach is based on characterizing the quality or well-functioning of collaboration using sequence alignment techniques that were originally developed in bioinformatics for the comparison of DNA sequences. Sequence alignment provides a similarity measure between pairs of action logs originating from the group work. The ensuing similarity matrix allows for clustering the working groups characterized by the similarity of their complete action logs. Notably, these clusters differ clearly in terms of quality measures related to the activity distribution and group output. This allows for determining quality indicators that can trigger feedback and adaptive scaffolding. Our data stem from inter-university online courses in which regular (weekly or bi-weekly) assignments had to be completed in small groups supported by an online forum for coordination and a separate tool for the actual collaborative writing. The basic actions are interpreted/coded as different contribution types, namely coordination, monitoring, minor/major contributions. Longer periods of inactivity are coded as another type of action (“gap”). These are the building blocks for the sequence alignment. One consequence of our analysis is a revision of the intuitive assumption that inactivity is a main indicator of inefficient group work: We found that inactivity can be compensated by early coordination to the extent that coordination is even the most important factor for successful group work. These insights are now being used to design alert mechanisms to signal inefficient group work and to generate timely interventions.
What are mathematics teachers' considerations in grouping students, and how could automated formative assessment systems help them in doing it? In this study, nine teachers were asked to use data on students' performance in a mathematics task, derived from an automated formative assessment system, to create pairs in which students could contribute to their peers. We called this grouping strategy "complementary." The teachers were also asked to explain their considerations for each grouping. We found two main grouping strategies in addition to the complementary one: based on similar answers ("similarity"), and based on dissimilar answers, in which one student performed better than another and could teach the other ("hierarchy"). Findings show that despite the experimenter's request to group students based on complementarity, teachers mostly grouped based on other considerations, at times even grouping students whose answers were complementary using hierarchical considerations. In some cases, different teachers formed the same groups of students based on different grouping strategies. The findings confirm the hypothesis that informed grouping may be challenging for teachers, and may benefit greatly from an automated pairing system.
One advantage of small group collaboration in online courses is that it can enrich the students learning experience with regard to interactional and social dimensions. In this paper we apply a previously tested method of sequential analysis on group activity sequences. These activity sequences stem from an online course on computer mediated communication where the group tasks consisted of collaborative text production. Students activities in a group forum and a shared wiki were recorded and classified as coordination, monitoring, major/minor contribution. Analyses of clusters of similar sequences show, that there are characteristic patterns indicating productivity, fair work distribution, as well as satisfaction with the group work. Our findings are a step towards automatic diagnosis of collaboration problems in online group work to facilitate early interventions.
A recent challenge in online learning courses is to establish collaboration in small groups. Group work in such courses often has to take place asynchronously, which puts additional requirements on communication and coordination to organise work in a productive way. This paper presents analyses of sequences of actions performed by such small learning groups during collaborative editing of texts in an online tool as part of a university-level online course. There were no face-to-face sessions and coordination between group members was supported by providing discussion forums. Actions of the group members in the forum or the writing tool were encoded as different contribution types, namely coordination, monitoring, minor contribution, and major contribution. Sequences of those actions derived for particular groups were used to explore the differences in the working process between the groups. It is partially possible to attribute those differences in the action sequences to the type of group composition (heterogeneous vs. homogeneous groups). Furthermore, initial evidence could be found that groups with inactive members had difficulties with coordination and tend to start the work late. These insights can be used to design mechanisms to diagnose defective group work and to generate interventions in online learning courses.
Online discussion forums have been shown to contribute to the trust and cohesion of groups, and their use has been associated with greater overall engagement in online courses. We devised two experimental interventions to encourage learners to participate in forums. A collectivist (“your participation benefits everyone”), individualist (“you benefit from participating”), or neutral (“there is a forum”) framing was employed to tailor encouragements for forum participation. An email encouragement was sent out to all enrolled users at the start of the course (study 1: general encouragement), and later in the course, to just those who had not participated in the forum (study 2: targeted encouragement). Encouragements were ineffective in motivating learners to participate. The collectivist framing discouraged contributions relative to the other framings and no encouragement. This prompts the question: To what extent do online learners experience a sense of community in current implementations of online courses?
In this paper we discuss the use of the I-Help peer help environment in a range of computer science courses at the University of Saskatchewan in the Fall of 2000. In particular we focus on the I-Help public discussion forums, an asynchronous help environment that stimulates students to help each other. A variety of evidence regarding the use of these forums was collected and analysed. Results suggest that I-Help is a useful resource for all kinds of user: those asking questions, those offering help and those reading postings.
As reported in summary form by W. Moede (1927), an unpublished study found that in a rope-pulling task, while collective group performance increased somewhat with group size, it was less than the sum of the individual efforts (IE). IE decreased as group size increased. The present 2 experiments with 84 undergraduates investigated this effect using clapping and shouting tasks. Results replicate the earlier findings. The decrease in IE, which is here called social loafing, is in addition to losses due to faulty coordination of group efforts. The experimental generality, theoretical importance, widespread occurrence, and negative social consequences of social loafing are examined, along with ways of minimizing it. (26 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Students collaborating in small groups is a characteristic of problem-based learning (PBL) that is receiving increased consideration in the literature. In this paper findings from studies in this area are synthesized and discussed. A distinction is made between studies focusing on cognitive effects of group learning and studies focusing on motivational effects of group learning. Studies concentrating on the cognitive effects of small-group PBL seem to demonstrate that activation of prior knowledge, recall of information, causal reasoning or theory building, cognitive conflicts leading to conceptual change and collaborative learning construction take place in the tutorial group. Studies focusing on the motivational effects of PBL demonstrate that group discussion positively influences students' intrinsic interest in the subject matter under discussion. The studies also demonstrate that a haphazard discussion in the tutorial group or a discussion that just scratches the surface, probably caused by students being less motivated, inhibits student learning. Several studies are reported providing suggestions on how to optimize group work in PBL. Although the studies demonstrate that group learning in PBL may have positive effects, much more research is needed to obtain more evidence and deeper insight in the cognitive and emotional effects of small group learning in PBL.
Moving beyond the general question of effectiveness of small group learning, this conceptual review proposes conditions under which the use of small groups in classrooms can be productive. Included in the review is recent research that manipulates various features of cooperative learning as well as studies of the relationship of interaction in small groups to outcomes. The analysis develops propositions concerning the kinds of discourse that are productive of different types of learning as well as propositions concerning how desirable kinds of interaction may be fostered. Whereas limited exchange of information and explanation are adequate for routine learning in collaborative seatwork, more open exchange and elaborated discussion are necessary for conceptual learning with group tasks and ill-structured problems. Moreover, task instructions, student preparation, and the nature of the teacher role that are eminently suitable for supporting interaction in more routine learning tasks may result in unduly constraining the discussion in less structured tasks where the objective is conceptual learning. The research reviewed also suggests that it is necessary to treat problems of status within small groups engaged in group tasks with ill-structured problems. With a focus on task and interaction, the analysis attempts to move away from the debates about intrinsic and extrinsic rewards and goal and resource interdependence that have characterized research in cooperative learning.
In this paper, we present early research evaluating the predictive power of a variety of temporal features across student subpopulations with distinctive behaviors at the beginning of the course. Initial results illustrate that these features predict important difierences across the subpopulations and over time in the courses. Ultimately, these results have implications for efiectively targeting adaptive scafiolding tailored to the particular intentions and goals of subpopulations in MOOCs.
This article reports on a study concerning secondary school students' meaning-making of socio-scientific issues in Information and Communication Technology-mediated settings. Our theoretical argument has as its point of departure the analytical distinction between 'doing science' and 'doing school,' as two different forms of classroom activity. In the study we conducted an analysis of students working with web-based groupware systems concerned with genetics. The analysis identified how the students oriented their accounts of scientific concepts and how they attempted to understand the socio-scientific task in different ways. Their orientations were directed towards finding scientific explanations, towards exploring the ethical and social consequences, and towards 'fact-finding.' The students' different orientations seemed to contribute to an ambivalent tension, which, on the one hand, was productive because it urged them into ongoing discussions and explicit meaning-making. On the other hand, however, the tension elucidated how complex and challenging collaborative learning situations can be. Our findings suggest that in order to obtain a deeper understanding of students' meaning-making of socio-scientific issues in Information and Communication Technology-mediated settings, it is important not only to address how students perform the activity of 'doing science.' It is equally important to be sensitive with respect to how students orient their talk and activity towards more or less explicit values, demands, and expectations embedded in the educational setting. In other words, how students perform the activity of 'doing school.'
Although many cooperative learning methods advocate grouping students heterogeneously in order to maximize the diversity of perspectives, skills, and backgrounds, past research shows that heterogeneous grouping generally ben-efits low-ability students but does not necessarily benefit high-ability students. This study investigates the effects of group ability composition (homogeneous versus heterogeneous) on group processes and outcomes for high-ability students completing science performance assessments. High-ability students working in homogeneous groups uniformly performed well, and high-ability students in some heterogeneous groups performed as well as high-ability students in homogeneous groups; but high-ability students in other heterogeneous groups did not perform as well. The quality of group functioning served as the strongest predictor of high-ability students’ performance and explained much of the effect of group composition.
Models of behavior and achievement in small group and individual settings are presented. These models arc based on intensive analyses of observations of groups and individuals at work. For each setting the varieties of behavior observed, social‐psychological mechanisms hypothesized to influence observed behavior, and factors hypothesized to relate behavior to learning are described. A synthesis of the models suggests that neither the group nor the individual setting is best for all persons; the benefits of a particular setting depend upon the experiences of the learner within that setting.
Fostering Discussion across Communication Media in Massive Open Online Courses
- O Ferschke
- I Howley
- G Tomar
- D Yang
- C Rosé
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