Effects of an online team project-based learning environment with group awareness and peer evaluation on socially shared regulation of learning and self-regulated learning

Recently, some studies proposed methods to promote socially shared regulation of learning (SSRL) level within a team because high SSRL levels enable an effective collaboration. Meanwhile, several studies also proposed methods in online collaboration to enhance individual self-regulated learning (SRL). Notably, most existing studies focused on proposing methods and tools either for enhancing SSRL level within a team or for enhancing individual SRL. A computer-supported collaborative learning (CSCL) environment with proper supports is promising for simultaneously enhancing the SSRL level within a team and individual SRL because SSRL and SRL have an inseparable relation and mutually influence during collaborative process. Based on the existing principles and theories, this work adopts the supports of group awareness and peer evaluation in CSCL with project-based learning. Group awareness (GA) can reveal collaborative behaviour of group members and regulate their participation while peer assessment (PE), which can appraise member’s contribution, can encourage individual responsibility and refine regulatory strategies. This study finds that the proposed group awareness and peer assessment (GAPE) (i.e. the experimental class) moderately reduces the free-rider effect and enhanced SSRL level and individual SRL, compared with NO-GAPE (i.e. the control group). Furthermore, this study also confirms that the perceived SSRL level can effectively predict individual SRL.     

Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments?

Research on self-regulated learning (SRL) in hypermedia-learning environments is a growing area of interest, and prior knowledge can influence how students interact with these systems. One hundred twelve (N = 112) undergraduate students’ interactions with MetaTutor, a multi-agent, hypermedia-based learning environment, were investigated, including how prior knowledge affected their use of SRL strategies. We expected that students with high prior knowledge would engage in significantly more cognitive and metacognitive SRL strategies, engage in different sequences of SRL strategies, spend more time engaging in SRL processes, and visit more pages that were relevant to their sub-goals than students with low prior knowledge. Results showed significant differences in the total use of SRL strategies between prior knowledge groups, and more specifically, revealed significant differences in the use of each metacognitive strategy (e.g., judgment of learning), but not each cognitive strategy (e.g., taking notes) between prior knowledge groups. Results also revealed different sequences of use of SRL strategies between prior knowledge groups, and that students spent different amounts of time engaging in SRL processes; however, all students visited similar numbers of relevant pages. These results have important implications on designing multi-agent, hypermedia environments; we can design pedagogical agents that adapt to students’ learning needs, based on their prior knowledge levels.     

Team formation instruments to enhance learner interactions in open learning environments

Open learning environments, such as Massive Open Online Courses (MOOCs), often lack adequate learner collaboration opportunities; they are also plagued by high levels of drop-out. Introducing project-based learning (PBL) can enhance learner collaboration and motivation, but PBL does not easily scale up into MOOCS. To support definition and staffing of projects, team formation principles and algorithms are introduced to form productive, creative, or learning teams. These use data on the project and on learner knowledge, personality and preferences. A study was carried out to validate the principles and the algorithms. Students (n = 168) and educational practitioners (n = 56) provided the data. The principles for learning teams and productive teams were accepted, while the principle for creative teams could not. The algorithms were validated using team classifying tasks and team ranking tasks. The practitioners classify and rank small productive, creative and learning teams in accordance with the algorithms, thereby validating the algorithms outcomes. When team size grows, for practitioners, forming teams quickly becomes complex, as demonstrated by the increased divergence in ranking and classifying accuracy. Discussion of the results, conclusions, and directions for future research are provided.     

Students’ perceptions of creativity in learning Information Technology (IT) in project groups

This paper explores students’ perceptions of creativity in learning Information Technology (IT) in project groups and the implications of better educating creative IT students for the future. Theoretically, the extension of social psychology research into creativity lays the basis of bringing creativity, learning and IT education into one framework. Empirically, qualitative interviews were carried out with 48 students from three disciplines, including Computer Science (n = 16), Electronic Systems (n = 15) and Medialogy (n = 17) at Aalborg University (AAU) in Denmark, which has a tradition of using problem-based learning (PBL) in student project groups. According to the findings, the students’ perceptions of creativity reflect their domain-related conceptualization and tacit learning experience, with different levels of confidence of being creative persons. As IT plays multiple roles in developing students’ creativity, it can be regarded as a ‘learning partner’. This implies that in the future creativity should be taught more explicitly, helping students to become creative IT talents as a part of their professional identity. It also requires teaching efforts to build a learning environment that stimulates creativity more effectively through more interactions between learners, learning tasks and learning tools.     

Effects of visual feedback on medical students’ procrastination within web-based planning and reflection protocols

Procrastination is a very common problem among students that results from ineffective selfregulation. In two field-experimental studies (N = 18 and N = 49), we investigated whether visual feedback on students’ previous procrastination was effective in provoking a decrease in students’ future procrastination as well as improvements in self-regulated learning. The visual feedback was implemented as a dynamic line chart in a web-based planning and reflection protocol used once a week by medical students to record their class preparation and homework once a week. In the protocols, the students planned and reflected on their personal learning processes and they estimated retrospectively their inclination to procrastinate. The results of both studies consistently showed that presenting students a line chart that adaptively visualizes the course and extent of their self-reported previous procrastination led to a statistically significant and practically relevant decrease in their future procrastination. Furthermore, the visualization had positive effects on other variables central to self-regulated learning. The studies provide converging evidence that the inclination to procrastinate can successfully be counteracted both by a parsimonious and easy-to-implement method. They are suggestive of ways how Internet technology can be used support students’ self-regulated learning.     

An automated signalized junction controller that learns strategies by temporal difference reinforcement learning

This paper shows how temporal difference learning can be used to build a signalized junction controller that will learn its own strategies through experience. Simulation tests detailed here show that the learned strategies can have high performance. This work builds upon previous work where a neural network based junction controller that can learn strategies from a human expert was developed (Box and Waterson, 2012). In the simulations presented, vehicles are assumed to be broadcasting their position over WiFi giving the junction controller rich information. The vehicle's position data are pre-processed to describe a simplified state. The state-space is classified into regions associated with junction control decisions using a neural network. This classification is the strategy and is parametrized by the weights of the neural network. The weights can be learned either through supervised learning with a human trainer or reinforcement learning by temporal difference (TD). Tests on a model of an isolated T junction show an average delay of 14.12 s and 14.36 s respectively for the human trained and TD trained networks. Tests on a model of a pair of closely spaced junctions show 17.44 s and 20.82 s respectively. Both methods of training produced strategies that were approximately equivalent in their equitable treatment of vehicles, defined here as the variance over the journey time distributions.     

The role of feedback and self-efficacy on web-based learning: The social cognitive perspective

The social cognitive perspective of self-regulated learning suggests that effective learning is determined by the interactions among personal, behavioral, and environmental influences; particularly, high self-regulated learners hold higher motivation (personal), apply better learning strategies (behavioral) and respond to environmental demand more appropriately (environmental). The study thus uses the social cognitive perspective to explore the role of self-efficacy (personal), student feedback behavior, use of learning strategies (behavioral), performance and receiving feedback (environmental) in Web-based learning. There were 76 university students participated in this study. Both quantitative and qualitative methods were applied for data analysis. The results supported that self-efficacy predicted student use of learning strategies and related to elaborated feedback behavior (personal → behavioral). High self-efficacy students applied more high-level learning strategies, such as elaborative strategy and critical thinking. Students who provided elaborated feedback also had higher self-efficacy than those who did not. Moreover, receiving elaborative feedback significantly promoted student self-efficacy (environmental → personal), while receiving knowledge of correct response improved student performance. However, the results indicated that feedback behaviors did not predict academic performance, which may be interfered by modeling effects.     

Early-stage atherosclerosis detection using deep learning over carotid ultrasound images

This paper proposes a computer-aided diagnosis tool for the early detection of atherosclerosis. This pathology is responsible for major cardiovascular diseases, which are the main cause of death worldwide. Among preventive measures, the intima-media thickness (IMT) of the common carotid artery stands out as early indicator of atherosclerosis and cardiovascular risk. In particular, IMT is evaluated by means of ultrasound scans. Usually, during the radiological examination, the specialist detects the optimal measurement area, identifies the layers of the arterial wall and manually marks pairs of points on the image to estimate the thickness of the artery. Therefore, this manual procedure entails subjectivity and variability in the IMT evaluation. Instead, this article suggests a fully automatic segmentation technique for ultrasound images of the common carotid artery. The proposed methodology is based on machine learning and artificial neural networks for the recognition of IMT intensity patterns in the images. For this purpose, a deep learning strategy has been developed to obtain abstract and efficient data representations by means of auto-encoders with multiple hidden layers. In particular, the considered deep architecture has been designed under the concept of extreme learning machine (ELM). The correct identification of the arterial layers is achieved in a totally user-independent and repeatable manner, which not only improves the IMT measurement in daily clinical practice but also facilitates the clinical research. A database consisting of 67 ultrasound images has been used in the validation of the suggested system, in which the resulting automatic contours for each image have been compared with the average of four manual segmentations performed by two different observers (ground-truth). Specifically, the IMT measured by the proposed algorithm is 0.625 ± 0.167 mm (mean ± standard deviation), whereas the corresponding ground-truth value is 0.619 ± 0.176 mm. Thus, our method shows a difference between automatic and manual measures of only 5.79 ± 34.42 μm. Furthermore, different quantitative evaluations reported in this paper indicate that this procedure outperforms other methods presented in the literature.     

Cognitive and affective effects of seductive details in multimedia learning

The present study integrates cognitive and affective aspects of media processing in order to make an argument for reexamining the current cold cognition perspective in multimedia research in favor of a more integrative perspective. The Cognitive-Affective-Theory-of-Learning-with-Media (CATLM) assumes that students need to become motivated to make full use of their cognitive resources. Therefore, and even though seductive details (sds) are additional interesting but unnecessary pieces of information that do not conform with the coherence principle, their possible motivational role should not be dismissed. Using a 2 × 3-experimental design, participants (N = 123) were asked to learn about biology with multimedia instruction that manipulated modality (text vs. narration) and presence of seductive details (no-sds vs. textual-sds vs. narrated-sds). Results of variance analyses show a modality effect. In addition, moderated mediation analyses with the moderator modality and mediator situational interest confirm the affective mediation assumption with the following two conditional effects. A direct detrimental effect of seductive details on learning performance under the text-condition and an indirect compensatory effect under the narration-condition were shown.     

Using online shared workspaces to support group collaborative learning

The ability to work collaboratively is highly valued in today’s workplace. However, collaboration does not often naturally happen in a group. Certain strategies must be applied to coordinate individual efforts and monitor the learning process so that effective collaboration can take place. 34 groups from 4 PGDE (Post Graduate Diploma in Education) classes at NIE (National Institute of Education) in Singapore participated in this study. Each group of four members used an online shared workspace to collaborate. Results showed that about half groups actively used the workspaces to share resources, negotiate ideas, and coordinate their collaboration. On the other hand, using the workspaces helped the teacher to easily track and monitor the collaborative learning process, as the workspaces documented what group members did and how they gradually completed the assignment. The students were also required to write weekly progress reports. Results showed that writing progress reports helped group members to reflect on what they had done and also provided additional information for the teacher to confirm their individual contributions. Issues involved in coordinating and monitoring the collaborative learning process are discussed.     

Recognizing people’s identity in construction sites with computer vision: A spatial and temporal attention pooling network

Several prototype vision-based approaches have been developed to capture and recognize unsafe behavior in construction automatically. Vision-based approaches have been difficult to use due to their inability to identify individuals who commit unsafe acts when captured using digital images/video. To address this problem, we applied a novel deep learning approach that utilizes a Spatial and Temporal Attention Pooling Network to remove redundant information contained in a video to enable a person’s identity to be automatically determined. The deep learning approach we have adopted focuses on: (1) extracting spatial feature maps using the spatial attention network; (2) extracting temporal information using the temporal attention networks; and (3) recognizing a person’s identity by computing the distance between features. To validate the feasibility and effectiveness of the adopted deep learning approach, we created a database of videos that contained people performing their work on construction sites, conducted an experiment, and then performed k-fold cross-validation. The results demonstrated that the approach could accurately identify a person’s identity from videos captured from construction sites. We suggest that our computer-vision approach can potentially be used by site managers to automatically recognize those individuals that engage in unsafe behavior and therefore be used to provide instantaneous feedback about their actions and possible consequences.     

Structuring peer assessment: Comparing the impact of the degree of structure on peer feedback content

The present study examines the added value of structuring the peer assessment process, by providing students with a peer feedback template with a varying structuring degree, for the peer feedback content quality in a wiki environment in higher education. The present study took place in the 1st year of a university course in Instructional Sciences (N = 176) and more specifically compared three conditions: no structure peer feedback (control), basic structure peer feedback, and elaborate structure peer feedback condition. Quantitative content analysis of students’ (n = 41) peer feedback messages was performed, and an analysis of (co)variance revealed some discrepancies between the conditions regarding the proportion of peer feedback content categories: (1) peer feedback style, (2) verification type, (3) verification focus, (4) elaboration type, and (5) elaboration focus. This study demonstrated that a higher structuring degree in a peer feedback template during the peer assessment process might have an impact on peer feedback content with respect to the above-mentioned categories; the peer feedback content. Results revealed significant differences between the three conditions regarding the peer feedback content categories. This study illustrated how a practical instructional intervention in the feedback process can increase the potential impact of peer assessment and boost students’ learning in higher education.     

Socially shared regulation of learning in CSCL: understanding and prompting individual- and group-level shared regulatory activities

The field of computer supported collaborative learning (CSCL) is progressing instrumentally and theoretically. Nevertheless, few studies examine the effectiveness and efficiency of CSCL with respect to cognitive, motivational, emotional, and social issues, despite the fact that the role of regulatory processes is critical for the quality of students’ engagement in collaborative learning settings. We review the four earlier lines in developing support in CSCL and show how there has been a lack of work to support individuals in groups to engage in, sustain, and productively regulate their own and the group’s collaborative processes. Our aim is to discuss how our conceptual work in socially shared regulation of learning (SSRL) contributes to effective and efficient CSCL, what tools are presently available, and what the implications of research on these tools are for future tool development.     

Predictive performance modeling for distributed batch processing using black box monitoring and machine learning

In many domains, the previous decade was characterized by increasing data volumes and growing complexity of data analyses, creating new demands for batch processing on distributed systems. Effective operation of these systems is challenging when facing uncertainties about the performance of jobs and tasks under varying resource configurations, e. g., for scheduling and resource allocation. We survey predictive performance modeling (PPM) approaches to estimate performance metrics such as execution duration, required memory or wait times of future jobs and tasks based on past performance observations. We focus on non-intrusive methods, i. e., methods that can be applied to any workload without modification, since the workload is usually a black box from the perspective of the systems managing the computational infrastructure. We classify and compare sources of performance variation, predicted performance metrics, limitations and challenges, required training data, use cases, and the underlying prediction techniques. We conclude by identifying several open problems and pressing research needs in the field.     

Exploration of the cognitive regulatory sub-processes employed by groups characterized by socially shared and other-regulation in a CSCL context

The current study examined how collaborative groups use cognitive regulatory sub-processes within a Computer-Supported Collaborative Learning (CSCL) context, and how these sub-processes were enacted differently for groups primarily characterized by their socially shared or other-regulation. Toward that end, the synchronous chat logs from three tasks of two collaborative groups, a socially shared relative to an other-regulation group, were qualitatively analyzed for planning, monitoring, and evaluation processes. Findings indicated that both groups maintained a high quality focus on content monitoring. The pathways for both groups differed, given the jointly coordinated planning and task monitoring of the socially shared group relative to the efficient but fully guided planning and task monitoring for the other-regulated group. Results suggest that content monitoring can play a key role in the high quality joint activity in both groups because of synergy among the cognitive regulatory sub-processes.     

Capturing the dynamic and cyclical nature of regulation: Methodological Progress in understanding socially shared regulation in learning

International Journal of Computer-Supported Collaborative Learning - Self-regulation is critical for successful learning, and socially shared regulation contributes to productive collaborative...     

Qualitatively different cognitive processing during online reading primed by different study activities

This article uses eye-tracking technology to examine how study activities such as taking notes or filling in a graphic organizer affect cognitive processing during learning. College students read a computer-presented passage that compared the characteristics of eastern steamboats (top section) and western steamboats (bottom section), either by reading it twice (read-only group), typing notes into a textbox on the right side of the screen (note-taking group), or typing characteristics of the two types of steamboats into a compare-and-contrast graphic organizer on the right side of the screen (graphic organizer group). Compared to the note-taking group, the graphic organizer group displayed more eye movements between the top and bottom of the passage (i.e., integrative saccades, d = 1.03), more eye movements between the text and the type-in window on the right side (i.e., constructive saccades, d = 0.79), fewer constructive saccades during initial reading (d = −0.64), and less time looking to the right side during initial reading (d = −0.81); and scored higher on a comprehension test given afterwards (d = 1.17), although both study groups outscored the read-only group. Results suggest that students in the note-taking group (and read-only group) tended to use a linear learning strategy in which their eyes followed the text in the order presented whereas students in the graphic organizer group tended to use a generative learning strategy in which their eyes searched for connections between specific information across the passage required to make comparisons.     

Physical and social presence in collaborative virtual environments: Exploring age and gender differences with respect to empathy

Collaborative virtual environments (CVEs) hold the immense potential of enhancing social inclusion and social support not only in younger but especially also in older people. However, there is still considerably little knowledge about the experiences of elderly when using CVEs. Additionally, there is reason to assume that men and women regardless of their age might also differ in their perceptions of CVEs, with this difference very likely being mediated by empathic abilities. Consequently, the main objective of the current study was to evaluate gender specific experiences of social and physical presence in a group of older (N = 62) and younger adults (N = 62) with respect to possible mediating influences of empathy. Results indicate no differences in physical and social presence between the two age groups, yet they support past findings that men experience more spatial presence, involvement and a higher sense of being there than women. Also, the empathy scale Fantasy considerably mediated gender differences in spatial presence, thus strengthening the theoretical assumption of a relationship between presence and empathy. Implications and future directions of these findings are discussed in detail.     

On the utility of pictorial feedback in computer-based learning environments

Extensive research has added to what is known about the nature of feedback and how to best incorporate it into instruction. Yet, many questions related to learner feedback remain unanswered. One problem of practical importance is the utility of incorporating semantically related pictures into the feedback. Decades of research on feedback have largely focused on the use of verbal feedback in written instruction. This research included two experiments. The first experiment (n = 63) addressed the incorporation of pictorial feedback into instruction; the second experiment (n = 69) extended this study through the use of a more ecologically valid intervention. Results suggest that the use of pictures in feedback did not influence learning any more than text-only treatments. A discussion and recommendations for future research are provided.     

Short- and long-term effects of students’ self-directed metacognitive prompts on navigation behavior and learning performance

This study seeks to promote learning in computer-based learning environments utilizing students’ self-directed metacognitive prompts. Such prompts are based on the idea of instructing students to design their own metacognitive scaffolds and learn with them afterward. In a pre-post experimental design, students in the experimental group (n = 35) were instructed to configure their own metacognitive prompts before learning whereas students in the control group (n = 35) learned without prompts. Log file analysis of navigation behavior indicates that students who learned with their individually designed, self-directed prompts visited relevant webpages significantly more often and spent a longer time on them compared with students in the control group. Moreover, participants in the experimental group attained better transfer performance immediately after learning. The long-term effect in transfer performance was even greater in a follow-up learning session conducted after three weeks without any instructional support in either group. These results are consistent with theories of metacognition and self-regulated learning and indicate that self-directed prompts can lead to sustainable effects.