An Interface Design for Axial Induction Motor

Asynchronous motors have been used extensively in the industry since the first discovery. Although the general working principles do not change, they can be made geometrically in the form of cylinders, spheres, cones and even discs. One of these geometries is known as axial flux machine topology which has a disc structure. Axial flux machines are remarkable in terms of their high efficiency, high power density and advantage in terms of compact structures. In this study, extended literature search of axial flux induction motor is given. In addition to the literature, axial induction motor design interface has been created by using MATLAB GUI software. This interface can communicate with ANSYS Maxwell. Thus, the dimensioning values of the machine calculated via the interface can be drawn automatically in Ansys Maxwell and it can perform numerical analysis. The obtained torque, current and efficiency data were evaluated

Collaborative peer feedback and learning analytics: theory-oriented design for supporting class-wide interventions

Although dialogue can augment the impact of feedback on student learning, dialogic feedback is unaffordable by instructors teaching large classes. In this regard, peer feedback can offer a scalable and effective solution. However, the existing practices optimistically rely on students' discussion about feedback and lack a systematic design approach. In this paper, we propose a theoretical framework of collaborative peer feedback which structures feedback dialogue into three distinct phases and outlines the learning processes involved in each of them. Then, we present a web-based platform, called Synergy, which is designed to facilitate collaborative peer feedback as conceptualised in the theoretical framework. To enable instructor support and facilitation during the feedback practice, we propose a learning analytics support integrated into Synergy. The consolidated model of learning analytics, which concerns three critical pieces for creating impactful learning analytics practices, theory, design and data science, was employed to build the analytics support. The learning analytics support aims to guide instructors' class-wide actions toward improving students' learning experiences during the three phases of peer feedback. The actionable insights that the learning analytics support offers are discussed with examples

A collaborative learning approach to dialogic peer feedback: a theoretical framework

Producción CientíficaFeedback has a powerful influence on learning. However, feedback practices in higher education often fail to produce the expected impact on learning. This is mainly because of its implementation as a one-way transmission of diagnostic information where students play a passive role as the information receivers. Dialogue around feedback can enhance students’ sense making from feedback and capacities to act on it. Yet, dialogic feedback has been mostly implemented as an instructor-led activity, which is hardly affordable in large classrooms. Dialogic peer feedback can offer a scalable solution; however, current practices lack a systematic design, resulting in low learning gains. Attending to this gap, this paper presents a theoretical framework that structures dialogic feedback as a three-phase collaborative activity, involving different levels of regulation: first, planning and coordination of feedback activities (involving socially shared regulation), second, feedback discussion to support its uptake (involving co-regulation), and last, translation of feedback into task engagement and progress (involving self-regulation). Based on the framework, design guidelines are provided to help practitioners shape their feedback practices. The application of the principles is illustrated through an example scenario. The framework holds great potential to promote student-centred approaches to feedback practices in higher education.Ministerio de Ciencia, Innovación y Universidades (Project TIN2017-85179-C3-2-R)Junta de Castilla y León (Project VA257P18)European Commission (Project grant 588438-EPP-1-2017-1-EL-EPPKA2-KA

Creating collaborative groups in a MOOC: a homogeneous engagement grouping approach

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

Creating collaborative groups in a MOOC: a homogeneous engagement grouping approach

Producción CientíficaCollaborative 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.Agencia Estatal de Investigación Española - Fondo Europeo de Desarrollo Regional (grants TIN2017-85179-C3-2-R / TIN2014-53199-C3-2-RJunta de Castilla y León - Fondo Europeo de Desarrollo Regional (grant VA257P18)Comisión Europea (grant 588438-EPP-1-2017-1-EL-EPPKA2-KA

Generating actionable predictions regarding MOOC learners' engagement in peer reviews

Peer review is one approach to facilitate formative feedback exchange in MOOCs; however, it is often undermined by low participation. To support effective implementation of peer reviews in MOOCs, this research work proposes several predictive models to accurately classify learners according to their expected engagement levels in an upcoming peer-review activity, which offers various pedagogical utilities (e.g. improving peer reviews and collaborative learning activities). Two approaches were used for training the models: in situ learning (in which an engagement indicator available at the time of the predictions is used as a proxy label to train a model within the same course) and transfer across courses (in which a model is trained using labels obtained from past course data). These techniques allowed producing predictions that are actionable by the instructor while the course still continues, which is not possible with post-hoc approaches requiring the use of true labels. According to the results, both transfer across courses and in situ learning approaches have produced predictions that were actionable yet as accurate as those obtained with cross validation, suggesting that they deserve further attention to create impact in MOOCs with real-world interventions. Potential pedagogical uses of the predictions were illustrated with several examples

Generating actionable predictions regarding MOOC learners’ engagement in peer reviews

Producción CientíficaPeer review is one approach to facilitate formative feedback exchange in MOOCs; however, it is often undermined by low participation. To support effective implementation of peer reviews in MOOCs, this research work proposes several predictive models to accurately classify learners according to their expected engagement levels in an upcoming peer-review activity, which offers various pedagogical utilities (e.g. improving peer reviews and collaborative learning activities). Two approaches were used for training the models: in situ learning (in which an engagement indicator available at the time of the predictions is used as a proxy label to train a model within the same course) and transfer across courses (in which a model is trained using labels obtained from past course data). These techniques allowed producing predictions that are actionable by the instructor while the course still continues, which is not possible with post-hoc approaches requiring the use of true labels. According to the results, both transfer across courses and in situ learning approaches have produced predictions that were actionable yet as accurate as those obtained with cross validation, suggesting that they deserve further attention to create impact in MOOCs with real-world interventions. Potential pedagogical uses of the predictions were illustrated with several examples.European Union’s Horizon 2020 research and innovation programme (Marie Sklodowska-Curie grant 793317)Ministerio de Ciencia, Innovación y Universidades (projects TIN2017-85179-C3-2-R / TIN2014-53199-C3-2-R)Junta de Castilla y León (grant VA257P18)Comisión Europea (grant 588438-EPP-1-2017-1-EL-EPPKA2-KA

Supporting Teachers in the Design and Implementation of Group Formation Policies in MOOCs: A Case Study

Producción CientíficaCollaborative learning strategies, which can promote student learning and achievement, have rarely been incorporated into pedagogies of MOOCs. Such strategies, when implemented properly, can boost the quality of MOOC pedagogy. Nonetheless, the use of collaborative groups in MOOCs is scarce due to several yet critical contextual factors (e.g., massiveness, and variable levels of engagement) that hamper the group formation process. Therefore, there is a need for supporting MOOC teachers in the design and implementation of group formation policies when implementing collaborative strategies. This paper presents a study where two instruments were used to explore solutions to this need: a guide to support teachers during the planning of the group formation, and a technological tool to help them implement the collaborative groups designed and to monitor them. According to the results of the study, the design guide made the teachers aware of the contextual factors to consider when forming the collaborative groups, and allowed teachers inform some configuration parameters of the activity (e.g., duration and assessment type) and the group formation (e.g., criteria and parameters needed to build the groups). The technological tool was successfully incorporated into the MOOC platform. Lessons learned from the findings of the study are shared and their potential to inform the design guide is discussed.Ministerio de Economía, Industria y Competitividad (Projects TIN2014-53199- C3-2-R and TIN2017-85179-C3-2-R)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA082U16)European Commission (Proyect 588438-EPP-1-2017-1-EL-EPPKA2-KA

Understanding student behavior and perceptions toward earning badges in a gamified MOOC

Producción CientíficaDespite the advantages of MOOCs, such as the open and free access to education, these courses are criticized for students’ lack of motivation and their high dropout rates. Gamification is a technique used to increase student motivation and engagement in small-scale educational contexts. However, the effects of gamification on student engagement have been scarcely explored in MOOC environments, and the findings so far are inconsistent. To address this gap, this research work examines the students’ behavior toward earning badges and how it relates to their engagement in a gamified MOOC. According to the results, the behaviors toward badges of the active students were generally positive and significantly correlated with other variables measuring their engagement (e.g., pageviews, submitted tasks, forum posts), although this positive behavior seems to decrease throughout the course. Additionally, students that reported high motivation by badges at the end of the course showed a higher engagement level than those that were not appealed by badges.European Regional Development Fund, under project grants TIN2014-53199-C3-2-R and TIN2017-85179-C3-2-RJunta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. Project VA082U16 and VA257P18)European Commission, under project grant 588438-EPP-1-2017-1-EL-EPPKA2-KA