Fostering self‐regulated learning in a blended environment using group awareness and peer assistance as external scaffolds

Most systems for training self‐regulated learning (SRL) behaviour focus on the provision of a learner‐centred environment. Such systems repeat the training process and place learners alone to experience that process iteratively. According to the relevant literature, external scaffolds are more promising for effective SRL training. In this work, group awareness and peer assistance are used as external scaffolds in the process of training SRL behaviour, enhancing opportunities for self‐reflection and stimulating and encouraging learners. This work further develops a system, called self‐regulated learning with group awareness and peer assistance (SRL‐GAPA). Experimental results reveal that SRL‐GAPA provides significantly more participation in online training tasks and learning activities, better SRL behaviour and better learning achievement than the traditional SRL system (i.e,, a learner‐centred environment). The SRL‐GAPA benefited poorly self‐regulated learners more than highly self‐regulated students. Some implications of this finding are discussed.     

Understanding mobile learning from the perspective of self‐regulated learning

Cognizant of the research gap in the theorization of mobile learning, this paper conceptually explores how the theories and methodology of self‐regulated learning (SRL), an active area in contemporary educational psychology, are inherently suited to address the issues originating from the defining characteristics of mobile learning: enabling student‐centred, personal, and ubiquitous learning. These characteristics provide some of the conditions for learners to learn anywhere and anytime, and thus, entail learners to be motivated and to be able to self‐regulate their own learning. We propose an analytic SRL model of mobile learning as a conceptual framework for understanding mobile learning, in which the notion of self‐regulation as agency is at the core. The rationale behind this model is built on our recognition of the challenges in the current conceptualization of the mechanisms and processes of mobile learning, and the inherent relationship between mobile learning and SRL. We draw on work in a 3‐year research project in developing and implementing a mobile learning environment in elementary science classes in Singapore to illustrate the application of SRL theories and methodology to understand and analyse mobile learning.     

The impact of team‐based learning on students with different self‐regulated learning abilities

Team‐based learning (TBL) stresses applying knowledge rather than absorbing knowledge in class; studies have investigated the use of TBL and its merits in different teaching courses (e.g., medical science and business). TBL is most effective when students learn autonomously before class. However, the ability of autonomous learning is highly associated with the ability of self‐regulated learning (SRL); most importantly, not every student possesses good (or high) SRL ability. Nevertheless, few studies have compared the effectiveness of TBL in students with different SRL abilities. To address this issue, this study analyzed approximately 90 students, whose course teaching involves office application software (Microsoft Excel). This study also developed an online TBL system (called Online TBL) to facilitate performing TBL and to collect the learning behaviours of students with different (high or low) SRL abilities on each TBL stage. The analytical results show that compared with the low‐SRL students, the high‐SRL students were more prepared for class because they spent more reviewing material and had better scores on personal uploaded Excel and Individual Readiness Assurance Test. From the feedback of the peer evaluation, the results also show that the high‐SRL students received more credits than the low‐SRL students did. The questionnaire survey revealed that both low‐SRL and high‐SRL students had a favourable impression of TBL. Further discussion is given to explain the above results.     

Supporting self‐regulated learning in computer‐based learning environments: systematic review of effects of scaffolding in the domain of science education

Despite the widespread assumption that students require scaffolding support for self‐regulated learning (SRL) processes in computer‐based learning environments (CBLEs), there is little clarity as to which types of scaffolds are most effective. This study offers a literature review covering the various scaffolds that support SRL processes in the domain of science education. Effective scaffolds are categorized and discussed according to the different areas and phases of SRL. The results reveal that most studies on scaffolding processes focus on cognition, whereas few focus on the non‐cognitive areas of SRL. In the field of cognition, prompts appear to be the most effective scaffolds, especially for processes during the control phase. This review also shows that studies have paid little attention to scaffold designs, learner characteristics, or various task characteristics, despite the fact that these variables have been found to have a significant influence. We conclude with the implications of our results on future design and research in the field of SRL using CBLEs.     

Measuring critical components of digital literacy and their relationships with learning

The growing prominence of the Internet, and other digital environments, as educational tools requires research regarding learners' digital literacy. We argue that two critical aspects of digital literacy are the ability to effectively plan and monitor the efficacy of strategies used to search and manage the wealth of information available online, and the knowledge to appropriately vet and integrate those information sources. Therefore, digital literacy requires effective self-regulated learning (SRL) skills, and availing epistemic cognition (EC). Although numerous researchers and scholars have examined the role of SRL in online learning (e.g., Efklides, 2011; Lee and Tsai, 2010, Williams and Hellman, 2004, Winters et al., 2008), there is a need for additional empirical research on how SRL and EC interact, and relate to learning in digital environments. In this study, we used a powerful, but little-used data collection methodology, think-aloud protocol (TAP) analysis, to investigate the relations among SRL, EC, and learning gains with 20 college students who studied vitamins on the Internet. We also contributed to the literature by exploring alternative techniques for preparing, analyzing, and representing these data, accounting for the strengths and challenges of TAPs. We found that, on average, participants did increase their understanding as a result of learning with the Internet, and that a data-driven approach to understanding relations among SRL, EC, and learning yielded the most powerful representation of these phenomena. Our study has implications for future research on digital literacy using TAPs, as well as the relative contribution of SRL and EC, as aspects of digital literacy, to online learning.     

Improving early prediction of academic failure using sentiment analysis on self‐evaluated comments

This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text‐based self‐evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information from student self‐evaluations yields a significant improvement in early‐stage prediction quality. The results also indicate the limited early‐stage predictive value of structured data, such as homework completion, attendance, and exam grades, due to data sparseness at the beginning of the course. Thus, applying sentiment analysis to unstructured data (e.g., self‐evaluation comments) can play an important role in improving the accuracy of early‐stage predictions. The findings present educators with an opportunity to provide students with real‐time feedback and support to help students become self‐regulated learners. Using the exploring results for improvement in teaching and learning initiatives is important to maintain students' performances and the effectiveness of the learning process.     

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.     

Gesture recognition and sensorimotor learning‐by‐doing of motor skills in manual professions: A case study in the wheel‐throwing art of pottery

This paper presents a methodological framework for the use of gesture recognition technologies in the learning/mastery of the gestural skills required in wheel‐throwing pottery. In the case of self‐instruction or training, learners face difficulties due to the absence of the teacher/expert and the consequent lack of guidance. Motion capture technologies, machine learning, and gesture recognition may provide a way of overcoming such issues. The proposed methodology is used to record and model expert gestures and then to compare this model in real time with the gestures performed by the learner. Differences in kinematic aspects such as hand distances are detected, and optical/sonic sensorimotor feedback is provided to the learner by the system, alerting him/her when errors occur and guiding him/her to achieve better results. In the case described here, the system was evaluated with 11 learners. With the use of our system, the gestural performance of learners during self‐training has been improved in comparison to cases of self‐training without computer assistance.     

Quality in e‐learning: a framework for promoting and assuring quality in virtual institutions

With the growing demand for e‐learning along with striving for excellence associated with globalization, there are worldwide calls for enhancing and assuring quality in e‐learning, specifically in the context of the developing countries. Such calls for quality enhancement, accountability, added value, value for money, self‐evaluation, and role players' satisfaction in higher education settings cannot go unheeded. Addressing the concerns regarding enhancing and assuring quality in e‐learning, a comprehensive e‐quality framework is developed by taking into account the pros and cons of the previous models, frameworks, and studies of e‐quality. This e‐quality framework offers a structured set of factors and benchmarks as a tool for practical quality work with e‐learning in virtual institutions.     

SN‐Learning: An exploratory study beyond e‐learning and evaluation of its applications using EV‐SNL framework

Social networks have intruded in human life by providing new technological innovations in a range of fields, including the education. The use of social networks in education has the potential to extend e‐learning and to introduce new forms of tutoring, communication, and collaboration between students and instructors. Thus, e‐learning is the threshold of Social Networking‐based Learning (SN‐Learning). SN‐Learning consists of a new term, introduced in this paper, and involves e‐learning systems with social networking characteristics or learning through social networking platforms. To this direction, the main objective of this paper is to present this new technological advancement emerged nowadays and to evaluate relevant applications of the last decade using our adjusted evaluation framework, EV‐SNL, in order to highlight their strengths and weaknesses regarding digital learning. The major finding is that SN‐Learning systems focus mainly on the incorporation of social features and do not provide yet personalized and adaptive tutoring. This research provides guidelines to computer science researchers on the design and implementation of SN‐Learning platforms using artificial intelligence and modelling techniques. Moreover, it can support teachers of different fields so that they can enhance their instruction with new technologies. There is scope for a lot of improvement.     

Using computer algebra systems in mathematical classrooms

Abstract This paper describes a research whose main focus is the use of Computer Algebra Systems (CAS) in mathematical classrooms and the didactical possibilities linked with its use. The possibilities of integrating Self-Regulated Learning (SRL) within the CAS environment are brought into focus. Forty-three Israeli students (mean age 13.3) were assigned to two learning algebraic groups. The first group received explicit meta-cognitive SRL with CAS (CAS + SRL); the second group was exposed to CAS without SRL (CAS). Empirical results from the experimental and case study designs revealed that (CAS + SRL) students outperformed (CAS) students on algebraic thinking and that (CAS + SRL) students regulated their learning more effectively.     

Effects of community of inquiry,learning presence and mentor presence on K-12 online learning outcomes

This study extends the community of inquiry (CoI) framework and self-regulated learning (SRL) theory through an exploration of the structural relationships among existing CoI variables, learning presence (i.e., self-efficacy and online SRL strategy) and learning outcomes in the context of K-12 online learning. To help understand the influence of K-12 mentoring – which is unique to online learning in the U.S. – mentor presence is also included. Structural equation modelling of 696 online 8th through 12th graders' survey responses and final grades showed that adding learning presence to the CoI framework helped to explain how these learners translated their online-learning perceptions into cognitive and affective learning outcomes. We also found that mentor presence significantly and positively predicted online SRL strategy, one of the two components of learning presence. Lastly, we established a connection between the CoI model and various types of learning outcomes that are indicators of K-12 online learning success – though it should be noted that important differences existed between a model based on final grades and two other outcome models. It is hoped that the processes identified in this study will be useful and relevant to K-12 online-learning institutions and educators seeking to improve their offering via a wide range of approaches.     

Robust and nonfragile consensus of positive multiagent systems via observer‐based output‐feedback protocols

This article investigates the consensus problem for positive multiagent systems via an observer‐based dynamic output‐feedback protocol. The dynamics of the agents are modeled by linear positive systems and the communication topology of the agents is expressed by an undirected connected graph. For the consensus problem, the nominal case is studied under the semidefinite programming framework while the robust and nonfragile cases are investigated under the linear programming framework. It is required that the distributed state‐feedback controller and observer gains should be structured to preserve the positivity of multiagent systems. Necessary and/or sufficient conditions for the analysis of consensus are obtained by using positive systems theory and graph theory. For the nominal case, necessary and sufficient conditions for the codesign of state‐feedback controller and observer of consensus are derived in terms of matrix inequalities. Sufficient conditions for the robust and nonfragile consensus designs are derived and the codesign of state‐feedback controller and observer can be obtained in terms of solving a set of linear programs. Numerical simulations are provided to show the effectiveness and applicability of the theoretical results and algorithms.     

A study to explore the effects of self‐regulated learning environment for hearing‐impaired students

Although computer‐ and Internet‐aided learning for hearing‐impaired students has been a trend in the modern days, a few studies were done to examine their effectiveness in strengthening learning motivation and, in turn, enhancing learning efficiency. This study attempts to establish an interactive self‐regulated learning environment and employs Attention, Relevance, Confidence, Satisfaction motivation model, hyperlink technology, modular concepts and online learning community to design teaching contents and presentation of teaching materials. An empirical study was conducted to examine the effects of hearing‐impaired students learning motivation and cognitive attitude of learning environment. The results showed that interactive and self‐regulated learning environment could effectively strengthen learning motivation and promote positive learning behaviour in hearing‐impaired students.     

An iterative Q‐learning scheme for the global stabilization of discrete‐time linear systems subject to actuator saturation

In this paper, we propose a model‐free algorithm for global stabilization of linear systems subject to actuator saturation. The idea of gain‐scheduled low gain feedback is applied to develop control laws that avoid saturation and achieve global stabilization. To design these control laws, we employ the framework of parameterized algebraic Riccati equations (AREs). Reinforcement learning techniques are developed to find the solution of the parameterized ARE without requiring any knowledge of the system dynamics. In particular, we present an iterative Q‐learning scheme that searches for a low gain parameter and iteratively solves the parameterized ARE using the Bellman equation. Both state feedback and output feedback algorithms are developed. It is shown that the proposed scheme achieves model‐free global stabilization under bounded controls and convergence to the optimal solution of the ARE is achieved. Simulation results are presented that confirm the effectiveness of the proposed method.     

Interactive topic modeling

Topic models are a useful and ubiquitous tool for understanding large corpora. However, topic models are not perfect, and for many users in computational social science, digital humanities, and information studies—who are not machine learning experts—existing models and frameworks are often a “take it or leave it” proposition. This paper presents a mechanism for giving users a voice by encoding users’ feedback to topic models as correlations between words into a topic model. This framework, interactive topic modeling (itm), allows untrained users to encode their feedback easily and iteratively into the topic models. Because latency in interactive systems is crucial, we develop more efficient inference algorithms for tree-based topic models. We validate the framework both with simulated and real users.     

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.     

Could a mobile‐assisted learning system support flipped classrooms for classical Chinese learning?

In this study, the researcher aimed to develop a mobile‐assisted learning system and to investigate whether it could promote teenage learners' classical Chinese learning through the flipped classroom approach. The researcher first proposed the structure of the Cross‐device Mobile‐Assisted Classical Chinese (CMACC) system according to the pilot survey and reviewed literature, and then adopted a quasi‐experimental design to understand whether the developed system could promote and support flipped classroom learning for classical Chinese. A total of 56 eleventh graders from two classes participated in the experiment. The learners in the experimental group learned classical Chinese with the flipped classroom learning strategy with the assistance of the CMACC system, while the control group adopted the flipped classroom learning strategy without using the CMACC system. The results reveal that all of the students improved their Chinese performance, but it was noticeable that the learners who used the CMACC system showed better motivation in terms of self‐directed preview learning, while those who only learned with the traditional textbooks tended to be more passive. In sum, the mobile‐assisted learning system added value in providing learners with opportunities to achieve anytime and anywhere flipped classroom learning. The integration of ubiquitous mobile learning technology and the flipped classroom strategy can be viewed as a critical factor leading to students achieving self‐regulated learning. It is also suggested that instructors should carefully take the targeted learners' cultural background and the availability of supporting learning devices into consideration so as to prevent the flipped classroom from exacerbating the digital divide. Other suggestions for educators and instructional designers are also proposed.     

Improving the quality of e‐learning: lessons from the eMM

The quality of e‐learning can be defined in many different ways, reflecting different stakeholders and the complexity of the systems and processes used in higher education. These different conceptions of quality can be mutually contradictory and, while politically significant, may also be beyond the direct control or influence of institutional leaders. The e‐learning maturity model (eMM) provides a framework for e‐learning quality improvement that measures the capability of institutions to sustainably engage in e‐learning and visualizes that capability in a way that assists leaders and managers using that information to undertake systematic and targeted improvements in their institution's e‐learning activities. This paper synthesizes the outcomes of multiple international eMM assessments to extract a set of starting points for institutions intending to improve the quality of e‐learning experienced by their students.     

Acceleration of finite‐time stable homogeneous systems

Stabilization rates of power‐integrator chains are easily regulated. It provides a framework for acceleration of uncertain multiple‐input–multiple‐output dynamic systems of known relative degrees (RDs). The desired rate of the output stabilization (sliding‐mode control) is ensured for an uncertain system if its RD is known, and a rough approximation of the high‐frequency gain matrix is available. The uniformly bounded convergence time (fixed‐time stability) is obtained as a particular case. The control can be kept continuous everywhere except the sliding‐mode set if the partial RDs are equal. Similarly, uncertain smooth systems of complete multiple‐input–multiple‐output RDs (ie, lacking zero dynamics) are stabilized by continuous control at their equilibria in finite time and are accelerated. Output‐feedback controllers are constructed. Computer simulation demonstrates the efficiency of the proposed approach.