No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Smart Pedagogy: Innovative Teaching and Learning Strategies in Engineering Education
... Thus, this paper defines smart education as "a studentcentric intelligent learning environment enriched with digital learning resources to provide smart pedagogies that support smart learners' personalised learning experiences anywhere at any time using smart portable device and linked across educational institution or training workforce through the advancement and superiority of smart and wireless technologies". Student-centric intelligent learning environment is developed using smart technologies focusing on students' needs and learning style that change the role of a teacher to a facilitator [24], [25], [34], [35]. The intelligent learning environment is enriched with digital learning resources which can be available in the smart physical classroom or virtual classroom and access using smart portable devices to provide smart pedagogies that support adaptive and personalised learning experiences anywhere at any time [27]- [31]. ...
... The learning theory underpins smart pedagogy informs and provides learning activities, assessment process and evaluation method. This can impact the learning behaviour of smart learners while using smart portable devices for the learning process [35], [36]. ...
... 2) Smart learning environment: this can either be smart physical classroom or virtual classroom that facilities learning process, and provides opportunities for students to learn using smart portable devices and smart objects and is develop using smart technologies [34], [35]. The smart learning environment provides and houses digital learning resources and smart pedagogical style based on learning theory. ...
... Para abordar estos problemas se encuentran en la literatura especializada diferentes propuestas (Uskov, V., et al., 2018), (Borrego, M., Foster, M. J., y Froyd, J. E., 2014), entre las que destaca el uso de modelos visuales. Min, K. J., Jackman, J., y Chan, J. C., (2014) concluyeron que el uso de dichos modelos visuales ayuda a los estudiantes a comprender conceptos abstractos y mejoran el aprendizaje en temas relacionados con la ingeniería industrial. ...
... Publicación semestral, TEPEXI Boletín Científico de la Escuela Superior Tepeji delRío, Vol. 11, No. 21 (2024) [1][2][3][4][5][6][7][8][9][10][11] ...
En este artículo se presenta el desarrollo e implementación de una plataforma para la enseñanza de temas relacionados con la asignatura de robótica industrial a nivel superior como la cinemática, la dinámica, la planificación de trayectorias y el diseño de controladores. La plataforma consiste de herramientas de simulación en 3D y el kit educativo de Lego MindstormsTM. El proceso de enseñanza se lleva a cabo en dos etapas, en la primera el estudiante aprende los conocimientos de la asignatura mediante la plataforma didáctica de simulación en 3D basada en el entorno de MATLABTM/SimulinkTM que utiliza la librería de SimMechanicsTM para importar modelos de robot industriales previamente desarrollados en el programa de SolidWorksTM que satisfacen ciertos objetivos de aprendizaje. En la segunda etapa, el estudiante desarrolla su propio diseño en SolidWorksTM utilizando las piezas CAD del kit educativo de Lego MindstormsTM que le permitan aplicar los conocimientos adquiridos, para finalmente, transferir su conocimiento a un entorno real al desarrollar la configuración física del robot manipulador utilizando las piezas físicas del kit Lego MindstormsTM.
... By providing hands-on learning experiences, opportunities for collaboration and feedback, and mentorship and support, students can develop the skills they need to excel in any field. Among them, Problem-Based Learning (PBL) has emerged as a popular pedagogical approach in recent years [7][8][9]. PBL is a studentcentered approach that emphasizes the development of critical thinking, problem-solving, and collaboration skills. It involves presenting students with real-world problems and challenging them to work together in multidisciplinary teams to find solutions [8]. ...
... It is crucial for graduates and students to enhance their value by developing a wider range of skills and adapting to the changing performance expectations. They need to capitalize on their traditional technical skills while also acquiring broader skills to remain competitive [7,10,11]. ...
... А ключовими завданнями освіти у XXI столітті названі розвиток наукової та інноваційної діяльності в освіті, формування мислення, орієнтованого на майбутнє та підвищення якості освіти на інноваційній основі. (Edeh, 2021, Olelewe, 2021, Pangandaman, 2019, Uskov, 2018. ...
Процеси світової глобалізації вносять суттєві корективи в сучасну систему освіти. Разом з тим, модернізація освітньої галузі сприяє появі нової термінології і вимагає осмислення інноваційних процесів у педагогічній теорії. Тому, згідно з метою даного дослідження, у статті визначено сутність дефініції «педагогічна інноватика» у доробках вітчизняних і зарубіжних вчених, виявлено загальне та специфічне у міркуваннях дослідників щодо цього феномену. Зокрема, встановлено актуальність визначення поняття «педагогічна інноватика» й динамічність утворення даної дефініції; визначено процесуальність і особистісний чинник педагогічної інноватики та її цінність як неодмінної складової педагогічного та суспільного розвитку; зазначені сфера дослідження педагогічної інноватики та її першочергові завдання як науки.
Крім того, названі передумови інноваційного процесу в Україні та необхідність впровадження освітніх інновацій в світовій практиці.
Доведено, що педагогічна інноватика, як важливий засіб досягнення нового рівня освіти, здійснюючи безпосередній вплив на всіх учасників інноваційного процесу, слугує теоретичним фундаментом для ефективних змін освітньої системи в цілому.
... The future of quality education with a high skills training value must be studentcentred with an intelligent and multidisciplinary educational system supported by adaptive learning programmes, collaborative methodologies, digital learning resources and STEAM technology training and adapted to Industry 4.0 (Uskov et al., 2018). It must also improve creativity, the visibility of learning outcomes and communication, motivation and interest in learning. ...
Game-based learning has common characteristics with creativity; play can promote imagination and divergent thinking skills, which help to solve problems and to communicate ideas or tasks that generate experience and knowledge. A game allows free thinking, leaves aside thinking focused on academic activity and helps to achieve the game’s goals.
... Since the industrial revolution that teaching and learning methods kept evolving, but over the last decades one aspect was kept almost constant -the classroom. The technology was progressively been added to the class, introducing tablets, computers, and interactive boards, essentially developing the concepts of Smart Education, Smart Pedagogy, and Smart Classrooms [1]. Those changes kept the room as the center of education, and despite the advances in many areas, online classes represented only a small portion of the total classes. ...
... Tikhomirov et al. (2015) defined the three dimensions of smart learning/education as (a) educational outcomes, (b) information and communication technologies (ICTs) and (c) organisational aspects. The educational outcome dimension reflects the skills that should be acquired through smart education, which include adaptation, awareness, logical reasoning, selflearning, anticipation and self-organisation (Uskov et al., 2018a). The ICT dimension reflects a set of ICT technologies for organising and managing learning progress, developing learning content, facilitating social interaction during the learning process and achieving mobility (Uskov et al., 2016(Uskov et al., , 2018b. ...
This paper reports the impact of COVID-19 on the practice and delivery of geotechnical and geoenvironmental engineering (GGE) education modules including lectures, lab sessions, student assessments, and research activities based on the feedback from faculty members in 14 countries/regions around the world. Faculty members have since adopted a series of contingent measures to enhance teaching and learning experience during the pandemic, which includes facilitating active learning, exploring new teaching content related to public health, expanding e-learning resources, implementing more engaged and student-centered assessment, and delivering high-impact integrated education and research. The key challenge faculty members are facing appears to be how to maximize the flexibility of learning and meet physical distancing requirements without compromising learning outcome, education equity, and interpersonal interactions in the traditional face-to-face teaching. Despite the challenges imposed by the pandemic, this could also be a good opportunity for faculty members obliged to lecture to rethink and revise existing contents and approaches of professing GGE education. Three future opportunities including smart learning, flipped learning, and interdisciplinary education are identified. The changes could potentially provide students with a more resilient, engaged, interactive, and technology-based learning environment.
... Nowadays, it is necessary for teachers to continue updating their class methodology, even when there are already established models of education, adapting to new tools that facilitate student learning [1][2][3]. More often, teachers utilize new technology-based instruments as a learning practice. ...
This research focused on developing a methodology that facilitates the learning of control engineering students, specifically developing skills to design a complete control loop using fuzzy logic. The plant for this control loop is a direct current motor, one of the most common actuators used by educational and professional engineers. The research was carried out on a platform developed by a group of students. Although the learning techniques for the design and implementation of controllers are extensive, there has been a delay in teaching techniques that are relatively new compared to conventional control techniques. Then, the hands-on laboratory offers a tool for students to acquire the necessary skills in driver tuning. In addition to the study of complete systems, the ability to work in a team is developed, a fundamental skill in the professional industrial area. A qualitative and quantitative analysis of student learning was carried out, integrating a multidisciplinary project based on modern tools.
... Traditional in-classroom education versus quality has almost had the same horizontal trend for decades, while teaching approaches based on smart pedagogy are progressively increasing the quality over the years (Uskov, Bakken, et al., 2017). As reported in van den Broek, 2012 andUskov et al., 2018 some innovative teaching strategies and learning styles, include Active Learning (AL), Game-based learning (GBL), Learning-By-Doing (LBD), Challenge-based learning (CBL), Massive Open Online Courses (MOOC), Collaborative Learning (CL), Crowdsourcing-based Learning (CWL), Flipped Classroom (FC), Learning through Augmented and Virtual Reality (LAVR), and others. All the abovementioned approaches can extensively benefit from the use of advanced technologies to produce enhanced learning materials supporting smart education. ...
The democratization and accessibility of low-cost devices for image acquisition and the development of highly automated procedures for orientation and dense image matching allow almost every person to be a potential producer of photogrammetric models. The diffusion of image-based technologies to produce 3D models amongst wider audiences entails however some risks, as the lack of critical awareness of the final quality of the outputs. Information and education about potentialities and limitations of reality-based digitization by photogrammetry may help spreading procedures and methods for the correct use of this technology. This paper presents the results of one of the funded projects within the 2018 ISPRS Capacity Building Initiatives “Education and training resources on digital photogrammetry”. The production of multimedia material for supporting smart educational teaching and learning approaches will be reported, as well as experiences on their application on case studies. Blended innovative teaching and learning pedagogical approaches have been tested, as Flipped Classroom (FC), Learning-by-doing (LBD), Collaborative Learning (CL), and Challenge-Based Learning (CBL), supported by multimedia tools for capacity-building and knowledge transfer. The implementation of multimedia materials for supporting teaching strategies resulted in the production of updated and engaging resources, as videos, tutorials, and datasets to be used during courses, workshops, and seminars targeted to different user groups. The combination of teaching strategies and multimedia supporting materials were tested within national and international projects, from academic courses to complete non-experts, from activities on the field to online and distance learning.
... Source: The authors. Uskov et al. (2018) state that a number of teaching and learning strategies have emerged in recent decades, but we must also invest in research on "those innovative approaches supporting the main concepts of Smart Education, Smart Classroom, and Smart Pedagogy", that is, their "smartness". Thus, they seek important elements of this practice's effectiveness in requirements such as: (a) Adaptivity, (b) sensing, (c) inferring, (d) anticipation, (e) self-learning, and (f ) self-organization. ...
This chapter intends to expand on the definition of educational curation, and
what we can understand from the theoretical–practical perspective of active
methodologies. The content within this study is related to empirical, theo
-
retical, and reflexive research methods that were carried out in classrooms of
students from the course ‘active learning and hybrid learning’, a class taught
in master’s degree programmes in Brazil.
To begin, there are some correlations that should be made clear: (i) Work
-
ing with active methodologies of which innovate practices are one of the
actions of smart pedagogy; (ii) educational curation promotes a relationship
between teaching and learning with the current events of the world, and
searches for more engaged, critical, and active students; and (iii) the curator
teacher is one who is continually improving himself, seeking new resources,
inside and outside the school, and values research proposals appropriate to his
students.
The present chapter, as a result of the empirical research of methodological
experiments, shows some orientations about its abilities, competences, and
digital fluencies. The authors used as references in this study are Bacich and
Moran (2018), Garcia (2017), Garcia and Czeszak (2019), and Vickery (2013),
among others.
... Smart Pedagogy (SmP) as mention in [56] is a set of innovative teaching strategies and learning styles. We expand this concept. ...
... Smart Pedagogy (SmP) as mention in [56] is a set of innovative teaching strategies and learning styles. We expand this concept. ...
... Some papers deal with Learning Analytics or Big Data but they do not explain the techniques used for data collection and analysis, or simply address the topic of Smart Education in a theoretical way [6,[28][29][30][31][32]. Others focus on technology, either for the display of information [10,13], the communication between devices [9], to calculate the position of people [33][34][35] or the implementation of AR and VR in education [36][37][38][39][40]. Another relevant information is the educational level addressed in the papers. ...
Research and development often move forward based on buzzwords. New terms are coined to summarize new developments, often with several interpretations and without a formal definition. The term Smart Education has been coined to represent a move forward in technology-enhanced education, but what is behind it? Does it represent something essentially different from the educational technologies used before? In this paper, we do a systematic literature review to understand how this term is used, what the technologies behind it are, and what promises are made. We conclude that although the term is fuzzy, there are indeed several developments available today that can make educational technologies much more adapted to the learner and therefore underpin the learning in a smarter way.
... The Gartner Analytics Ascendancy Model [2] requires active and consecutive use of four types of analytics for comprehensive data analysis, including a) descriptive and diagnostic analytics based on available data about events/processes in the past, and b) predictive and prescriptive analytics for the prediction/forecasting of events/processes/outcomes in the future. This model creates a solid foundation for the conceptual modeling and engineering of Smart Learning Analytics (SLA) systems that would have and actively use the unique features of Smart University, Smart Education, and Smart Classrooms, including 1) adaptivity, 2) sensing, 3) inferring, 4) anticipation, 5) self-learning, and 6) self-organization [3][4][5]. ...
In engineering higher education, producing graduates with industry-ready techno-managerial skills in a very dynamic and rapidly changing industry/employer ecosystem is a huge challenge for higher education institutes (HEIs). Conventional teacher-centric classroom mode of teaching may not be apt rather a more effective student-centric skill-acquisition and outcome-based Teaching learning process (TLP) with modern pedagogical approach with a 360-degree quality life-cycle for continuous improvement would effective. In this paper, a Smart pedagogical learning and assessment methodology (SPLAM) is proposed by integrating Outcome based Education (OBE) framework and this fusion is coined by authors as SPLAM-OBE framework. SPLAM is proposed using five state-of-the-art strategies like active-learning, experiential-learning, technology-aided-learning, flipped-classroom based learning, problem-solving and critical-thinking based learning is need of the hour for engineering higher education programs offered by HEIs. This fusion design of SPLAM-OBE complies with the OBE framework requirements of National Board of Accreditation in India and some predominant international program accreditation agencies like Institute of Engineering & Technology U.K. and Accreditation Board for Engineering and Technology U.S.A. We have proposed SPLAM-OBE framework and implementation strategies with a case-study and validated the quality cycle and continuous improvement complying to program accreditation requirement by integrating smart pedagogical approach for effective TLP with a mission towards producing better skilled engineering graduates by better attainments of graduate attributes (GAs) and various outcome attributes like Course Outcomes (COs), Program Outcomes (POs), Program Specific Outcomes (PSOs), Program Educational Objectives (PEOs) and mission and vision statements of school/department and University/institute demonstrating 360 quality life-cycle.
The purpose of the article is to consider marketing methods of analysis for development of one of the regions in Russia for assessment the current state of automotive industry, which is the flagship for the economy of the tested region, and the impact of this assessment on the content for training the specialists and the graduates for the considered industry. The analysis of the organizational and marketing research enabled the authors to make a conclusion about the lack of marketing information about the automotive industry peculiarities. A forecast scenario for the development of the machine-building cluster until 2025, taking into account the specifics of functioning at the present time and the marketing data obtained in the study, is proposed. The obtained results can be used to optimize the production processes at businesses as well as the staff training for the car production and mechanical engineering.KeywordsMarketingMarket researchAutomotiveEngineeringMarket analysis
The aim of this paper is to consider management tools for sound planning, control and regulation of students’ competence level based on mathematical simulation methods. Innovative ideas include experimental planning methods applied for minimizing differences of the final results in educational systems in response to uncontrollable factors, while maximizing the possibility of obtaining a guaranteed and targeted result. The proposed model has been tested in the conditions of smart education in the process of learning on the basis of remote technology for distant education “Rosdistant”.KeywordsManagement and mathematic simulationQualitative and quantitative estimatesMultivariate normal distributionStatistical methods
Aim: This research aims to extract and categorize the components of an intelligent university to provide a conceptual framework.
Methodology: The current qualitative research was developed using a meta-composite strategy. Therefore, the specialized keywords of the research, Smart university, smart higher education, digital university, digital higher education in reliable scientific bases, Science Direct, Springer, Wiley Online Library, ERIC, Sage Journals, and Emerald were searched in the time frame (2010 - 2020). Among the 75 articles obtained, 25 made it to the final analysis. To achieve the research goal, what are the three questions of main components of the conceptual framework of the smart university? What factors have caused the emergence of complex conditions and the emergence of a smart university? What are the underlying factors for establishing a smart university? It was planned.
Findings: The findings in identifying the components of a smart university include: smart pedagogy (smart teaching, smart learning, smart assessment); Smart research (intelligence of research, capacity building of research), smart academic entrepreneurship (continuous interaction with industry, consulting and career services); Smart governance (good governance based on intelligentization, intelligent leadership of higher education); Smart modern management (smart management of higher education, smart management in universities); Smart campus (smart class, smart environment, green and smart sustainability of the campus, smart architecture in the campus); Smart technologies (technical-statistical, high-level technologies, smart rules and regulations); Smart capacity building (development of hard and technological infrastructure); Smart finance (mechanisms for providing and allocating financial resources, smart financial management) and smart human resources (smart faculty members, smart students, smart employees and smart human resources management). In response to the second question, three factors; cultural contexts (change in academic and extra-academic culture); motivational-attitudinal fields (motivational field, attitudinal field); competitive fields (intensification of competition inside and outside higher education), and dual factors; The proliferation of technological transformations (the development of non-educational technologies, the development of educational technologies), the emergence of biological changes (biological changes, academic changes) had an effect on the smart-university. Then, in addition to the theme network, the three-dimensional conceptual framework of the smart university is also drawn.
Conclusion: All in all, due to the developments in the fields of technology and the change in the structure and nature of past academic models, and the move towards smart universities, along with the occurrence of unknown events such as the Covid-19 epidemic, it has made it difficult to return to the past academic practices. However, in order to align with these developments, willingly or unwillingly, higher education systems have moved towards intelligentization for survival and activism in dynamic conditions. Based on the ten components identified in this research, higher education policymakers and university managers can formulate and implement strategies and programs for making universities smarter according to this component.
As a result of the current global pandemic, universities needed to rapidly convert to hybrid and distance learning. An international, cooperative course on global software engineering, jointly held by two universities, one in Japan and one in Germany, is described. Challenges posed when conducting a distributed course simultaneously in-person and online are described. Experiences reported by students are described. The results of the course are evaluated according to Smart Education levels.KeywordsSmartEducationGlobalSoftwareEngineeringHybrid
Risk management is known as a complex process both for business and education organizations. In order to organize the risk management process in accordance with standards and requirements, it is necessary to identify risks, the causes of their occurrence, determine the consequences of possible damage and define corrective actions aimed at reducing the risk situations occurrence. The purpose of this study is to develop a risk management model for the development of an educational organization based on the integration of some tools of standardization, digitalization and lean production. The integration model will improve the decision-making process through quantitative and qualitative analysis at each stage of uncertainty. The key advantage of the study is the risk management model, which allows establishing a causal relationship between the causes of risks and their consequences. The model is simulated in accordance with the requirements of the international standard ISO 31000-2018.
Prior research has indicated that smart pedagogy will be utilized in leading academic institutions in the near future. Based on a review of the literature on both smart pedagogy and Extended Reality (XR) in the fields of education, a research methodology was devised, and a comprehensive survey was distributed to Interactive Media students at Bradley University in Peoria, IL who responded to prompts based on their experiences and several XR prototypes that were programmed by one of the co-authors. Analysis of the responses demonstrated that students are extremely interested in utilizing XR within their classes and feel that XR-based smart pedagogy would provide an excellent complement to traditional lectures and out-of-class experiences rating higher than any of the other components of Smart Pedagogy by themselves.
The paper deals with innovative approaches for tertiary education system based on simulation of crowdsourcing model in integration with such innovative learning strategies as 1) adaptive learning; 2) learning by doing; 3) project-based learning; 4) contextual learning. The publication reveals the crowdsourcing model for effective performance in universities of the Russian Federation and Kazakhstan. The results of research, analysis and approbation of implemented components of smart pedagogy reflect different levels for the achieved “smartness” for tertiary education systems. The key indicators for smart classes including (1) adaptability, (2) self-learning and (3) self-organization have been considered. The conduced review, experiment and the feedback represent the crowdsourcing model based on Ed Tech as an effective way to transfer knowledge from the older generation to the younger one.
KeywordsEd-techTeaching strategyE-learningCrowdsourcing simulation
Smart pedagogy is a set of innovative teaching strategies, methods and practices to provide smart features - adaptation, sensing, inferring, self-learning, anticipation and self-optimization - for educational processes, objects, and environment. This paper presents the outcomes of long-term project at the Department of Computer Science and Information Systems and the InterLabs Research Institute at Bradley University (IL, USA) to develop, implement, test and refine various innovative teaching strategies as components of smart pedagogy for Science, Technology, Engineering and Mathematics (STEM) courses. The ultimate goal of smart pedagogy is two-fold: a) increase student interest in learning activities and overall satisfaction of learning process, and b) maximize the effectiveness of teaching and quality of learning outcomes in STEM courses. A set of implemented and thoroughly tested innovative teaching strategies include 1) Learning-by-Doing, 2) Crowdsourcing, 3) Smart Learning Analytics, 4) Flipped Classroom, 5) Collaborative Learning, and 6) Gamification/Badging based approaches. The obtained student feedback about implemented innovative teaching strategies undoubtedly demonstrate students’ keen interest in active use of smart pedagogy in STEM courses.KeywordsSmart pedagogyInnovative teaching strategiesStudent feedbackComparative analysisSTEM courses
In the dynamic world that is associated with unpredictable informatization and globalization, it is indispensable for education to be sustainable and continuously evolve to facilitate in fulfilling the learners' needs in the current digital technologies and knowledge era. In this chapter, the author discussed how innovative learning and education practices for modern pedagogy have been developed in the European universities system. Specifically, the author provides a delineation of the attributes, pedagogy, ideology, ethical dimensions, and practical applications of innovative learning education practices. In the chapter, the author identified the different practices such as massive open online courses (MOOCs), blended learning, and flipped classrooms that have been applied at different European universities. Thus, the information discussed in this chapter is relevant to university educators and electronic learning practitioners.
This chapter presents a framing discussion around the notion of a digital enterprise in the context of higher education. The chapter makes the assumption that a university like a commercial enterprise can draw significant benefit from acting as a digital enterprise. The discussion indicates that some of a university's existing and historical activities are in line with notions of a digital enterprise. The chapter proposes a framework for assessing the readiness of a university with respect to its actions as a digital enterprise recognising the complexity of domains residing within the confines of a university environment. Critically, the chapter argues that such a future systems project should not only consider positive use cases but also recognise that a digital enterprise may have unplanned and unintentional consequences. Hence, this chapter argues that new forms of governance may also be required alongside the planned journey to a digital enterprise world.
Learning Analytics is a dynamic interdisciplinary field that encompasses educational sciences and state-of-the-art technology, methods and systems from various fields of computing such as data science, data visualization, software engineering, human-computer interaction, statistics, artificial intelligence, with various stakeholders, e.g. instructors, students, department and college administrators, practitioners, university top managers, computer scientists, IT experts, and software developers. Despite some current achievements and initial developments in Learning Analytics, we are still in a very early stage of development of sophisticated technologies and well-thought practices, tools and applications in this field as well as understanding the impact of Learning Analytics on (a) student learning and privacy, and (b) faculty instruction and autonomy. This paper presents the up-to-date research findings and outcomes of a multi-aspect project on Smart Learning Analytics at Bradley University (USA). It describes the obtained research outcomes about student perception and attitude to Learning Analytics on an academic course level and corresponding Learning Analytics-based pedagogy.
Self-driving cars are technologically a reality and in the next decade they are expected to reach the highest level of automation. While there is general agreement that an advanced human-autonomous vehicle (HAV) interaction is key to achieve the benefits of self-driving cars, it is less clear what role artificial intelligence (AI) should play in this context. While the scientific community is debating on the role and intersections of AI, autonomous vehicles and related issues, above all ethics, the automotive industry is already presenting AI-based products and services that may influence, in a direction or in another, our technological and societal futures. This paper focuses on virtual assistants, the personification of the car intelligence incorporating, among others, an algorithmic “brain”, a synthetic human “voice” and powerful sensor-based “senses”. Should virtual assistants just assist humans or replace them whenever necessary? Should their scope of action be limited to safety-related driving tasks or to any activity performed in the car or controlled from the car? Although at a very early stage of commercial development, the paper will review the state-of-the-art of in-car virtual assistants underlining their role and functions in the connected and automated driving ecosystem. By drawing from earlier reflections on automation, robots and intelligent agents, it will then identify a series of issues to be addressed by the scientific community, policy-makers and the automotive industry stakeholders.
We introduce an independent open learner model for formative assessment and learning analytics based on developments in technology use in learning, also maintaining more traditional numerical and qualitative feedback options.
The rapid progress of mobile, wireless communication and sensing technologies has enabled the development of context-aware ubiquitous learning (u-learning) environments, which are able to detect the real-world learning status of students as well as the environmental contexts. Accordingly, appropriate information can be provided to individual students in the right place and at the right time. However, researchers have indicated that, to support students to learn in real-world contexts in smart ways, more factors need to be taken into account when designing and developing learning systems. In this paper, the definition and criteria of smart learning environments are presented from the perspective of context-aware ubiquitous learning. A framework is also presented to address the design and development considerations of smart learning environments to support both online and real-world learning activities. Moreover, some emerging technologies that might facilitate the development of smart learning environments as well as the features and criteria of smart learning are addressed. Finally, research issues related to smart learning are provided.
In this paper we analyze the current situation of education in universities, with particular reference to the European scenario. Specifically, we observe that recent evolutions, such as pervasive networking and other enabling technologies, have been dramatically changing human life, knowledge acquisition, and the way works are performed and people learn. In this societal change, universities must maintain their leading role. Historically, they set trends primarily in education but now they are called to drive the change in other aspects too, such as management, safety, and environment protection. The availability of newer and newer technology reflects on how the relevant processes should be performed in the current fast changing digital era. This leads to the adoption of a variety of smart solutions in university environments to enhance the quality of life and to improve the performances of both teachers and students. Nevertheless, we argue that being smart is not enough for a modern university. In fact, universities should better become smarter. By “smarter university” we mean a place where knowledge is shared between employees, teachers, students, and all stakeholders in a seamless way. In this paper we propose, and discuss a smarter university model, derived from the one designed for the development of smart cities.
Building on an earlier 2008 summary prepared for OECD by Marlene Scardamalia and Carl Bereiter, this paper by Gesa S. E. van den Broek provides a more extensive discussion of approaches described as “research based innovation.” Fostering Communities of Learning is a constructivist approach in which teachers help students discover important curricular concepts. Learning by Design is an inquiry-based science learning programme based on case-based reasoning models. Central Conceptual Structures (CCS) theory describes developmental changes in children’s thinking and what is needed to progress through stages in specific cognitive domains. Web-based Inquiry Science Environment (WISE) is an internet-based adaptive learning environment building on the principles of knowledge integration. Cognitive Tutors and ACT-R theory are intelligent adaptive software programmes that provide students with scaffolded instruction and feedback. Direct Instruction aims to accelerate learning through clear scripted direct instruction by the teacher and scaffolded practice aimed at student involvement and error reduction. Higher Order Thinking Skills (HOTS) is for disadvantaged students especially to engage in Socratic dialogues about ideas and strategies to solve computer game-based problems. Knowledge Building is a constructivist teaching approach centred on building knowledge and creating knowledge communities.S’inspirant d’une synthèse précédente rédigée par Marlene Scardamalia et Carl Bereiter pour l’OCDE en 2008, la présente note, de Gesa S. E. van den Broek, propose une réflexion plus large sur les approches relevant de ce que l’on appelle « l’innovation fondée sur la recherche ». Encourager les communautés apprenantes s’inscrit dans une démarche constructiviste selon laquelle les enseignants aident leurs élèves à découvrir des concepts importants du programme scolaire. Learning by Design est un programme d’apprentissage des sciences à partir d’enquêtes et de modèles de raisonnement fondés sur des études de cas. La théorie des structures conceptuelles centrales décrit l’évolution développementale du raisonnement des enfants et ce qui est nécessaire pour progresser et franchir des étapes dans des domaines cognitifs particuliers. WISE (Web-based Inquiry Science Environment) est un environnement pédagogique adaptatif sur internet qui repose sur les principes de l’intégration des connaissances. Les tuteurs cognitifs et la théorie ACT-R sont des logiciels adaptatifs intelligents qui proposent aux élèves une instruction et des retours d’information étayés. L’instruction directe vise à accélérer l’apprentissage grâce à des cours clairs, structurés et directs prodigués par l’enseignant, ainsi qu’à travers une application pratique et documentée favorisant la participation des élèves et la diminution des erreurs. Le programme HOTS (Higher Order Thinking Skills), destiné aux élèves de milieux défavorisés, a notamment pour objectif d’organiser des échanges d’idées et de stratégies en vue de résoudre des problèmes à partir de jeux électroniques. Le renforcement des connaissances est une approche pédagogique constructiviste axée sur le développement des connaissances et la création de communautés du savoir.
This book gathers the contributions presented at the 4th International KES Conference on Smart Education and Smart e-Learning (KES-SEEL-17), which took place in Vilamoura, Algarve, Portugal, June 21–23, 2017.
Smart education and smart e-Learning are emerging and rapidly growing areas. They represent the innovative integration of smart systems, technologies and objects, smart environments, smart pedagogy, smart learning and academic analytics, various branches of computer science and computer engineering, and state-of-the-art smart educational software and/or hardware systems.
It contains a total of 48 peer-reviewed book chapters that are grouped into several parts: Part 1 – Smart Pedagogy, Part 2 – Smart e-Learning, Part 3 – Systems and Technologies for Smart Education, Part 4 – Smart Teaching, and Part 5 – Smart Education: National Initiatives and Approaches.
The book offers a valuable source of research data, information on best practices, and case studies for educators, researchers, Ph.D. students, administrators, and practitioners—and all those who are interested in innovative areas of smart education and smart e-Learning.
This book presents peer-reviewed contributions on smart universities by various international research, design and development teams. Smart university is an emerging and rapidly evolving area that creatively integrates innovative concepts; smart software and hardware systems; smart classrooms with state-of-the-art technologies and technical platforms; smart pedagogy based on modern teaching and learning strategies; smart learning and academic analytics; as well as various branches of computer science and computer engineering.
The contributions are grouped into several parts: Part 1—Smart Universities: Literature Review and Creative Analysis, Part 2—Smart Universities: Concepts, Systems and Technologies, Part 3—Smart Education: Approaches and Best Practices, and Part 4—Smart Universities: Smart Long Life Learning. The book is a valuable source of research data and findings, design and development outcomes, and best practices for faculty, scholars, Ph.D students, administrators, practitioners and anyone interested in the rapidly growing areas of smart university and smart education.
This book contains the contributions presented at the 3rd international KES conference on Smart Education and Smart e-Learning, which took place in Puerto de la Cruz, Tenerife, Spain, June 15-17, 2016. It contains a total of 56 peer-reviewed book chapters that are grouped into several parts: Part 1 - Smart University: Conceptual Modeling, Part 2 – Smart Education: Research and Case Studies, Part 3 – Smart e-Learning, Part 4 – Smart Education: Software and Hardware Systems, and Part 5 – Smart Technology as a Resource to Improve Education and Professional Training. We believe that the book will serve as a useful source of research data and valuable information for faculty, scholars, Ph.D. students, administrators, and practitioners - those who are interested in innovative areas of smart education and smart e-learning.
The research objectives of the article consist in the identification of the strategic elements which contribute to the transformation of TrU into SmU and of the characteristics of e-learning Tesys platform which improve the performances of the students and professors. In our research we have started from the premises that smart technology is a strategic tool for the development of critical thinking and the Smart curricula and the Smart pedagogy have to be created in order to develop the critical thinking for both students and professors. Our findings revealed that INCESA HUB has the possibility to optimize learning process and to transform the University of Craiova into a modern and SmU. The e-learning Tesys platform plays a very important role, because, through its flexibility, it is able to fulfil the Smart mission of SmU. Finally, we arrived at the conclusion that the solution resides in the involvement of all stakeholders at intern, local, national and international level in order to acquire and share the knowledge.
Many principles of smart education are not explained now because of ambiguity of this concept. The system of key features of smart education is proposed in this article. It is one of possible ways to create the well-rounded paradigm of smart education. It means that it is necessary to take into account different aspects of this phenomenon, not only ICT components. Educational projects, which represent the vast majority of key components of smart education, can be regarded as the parts of smart educational trend. Three of the most important components – main dimensions of smart education - are identified and analyzed in this paper: educational outcomes, ICT, and organizational dimensions.
Fast proliferation of various types of smart devices, smart systems, and smart technologies provides academic institutions, students and learners with enormous opportunities in terms of new approaches to learning technologies, education, learning processes and strategies, corporate training, user’s personal productivity and efficiency, and faster and better quality of services provided. This paper presents the developed ontology of Smart Classroom systems - it helps to understand and analyze current smart classroom systems, and identify features, hardware, software, services, pedagogy, teaching and learning-related activities of the next generation Smart Classroom systems.
Globalization and innovation in technology have led to the extensive use of the latest technology in almost every sector, and education is no exception. Different technologies have been employed in various disciplines within the educational sector. Studies have shown that technology can enhance teaching and learning experiences. Augmented Reality (AR) is a new technology with vast potentials and great pedagogical value that offers new methods for education. AR enables the overlaying of computer-generated virtual information into the real environment in real time. Thus, researchers believed that the AR has provided new opportunities for designing engaging learning environments. Although the AR may improve educational outcomes, the main factor is to understand the process of designing the AR to support learning activities. Thus, various instructional strategies such as collaborative learning, were considered when designing an AR learning environment. Collaborative learning permits students to engage with other students and the educational content at the same time, resulting in a deeper understanding and higher motivation. Because educational research concerning collaborative AR is still in its infancy, this paper intends to review the literatures concerning collaborative AR, its previous usages and its potential in educational context.
Strategies for Education Innovation
- P Nair