Search results for: learning through making

Authors: Zheni S. Stefanova, Kandethody M. Ramachandran

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With the increasing dependency on our computer devices, we face the necessity of adequate, efficient and effective mechanisms, for protecting our network. There are two main problems that Intrusion Detection Systems (IDS) attempt to solve. 1) To detect the attack, by analyzing the incoming traffic and inspect the network (intrusion detection). 2) To produce a prompt response when the attack occurs (intrusion prevention). It is critical creating an Intrusion detection model that will detect a breach in the system on time and also challenging making it provide an automatic and with an acceptable delay response at every single stage of the monitoring process. We cannot afford to adopt security measures with a high exploiting computational power, and we are not able to accept a mechanism that will react with a delay. In this paper, we will propose an intrusion response mechanism that is based on artificial intelligence, and more precisely, reinforcement learning techniques (RLT). The RLT will help us to create a decision agent, who will control the process of interacting with the undetermined environment. The goal is to find an optimal policy, which will represent the intrusion response, therefore, to solve the Reinforcement learning problem, using a Q-learning approach. Our agent will produce an optimal immediate response, in the process of evaluating the network traffic.This Q-learning approach will establish the balance between exploration and exploitation and provide a unique, self-learning and strategic artificial intelligence response mechanism for IDS.

Keywords: cyber security, intrusion prevention, optimal policy, Q-learning

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Authors: Harleen Kaur, Deepika Jindal Singla

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Buying decision making is a complicated process, in which consumer’s decision is under the impact of others. The buying decision making is directed in a way that they have to act as customers in the society. Media and family are key socialising agents for adolescents’. Moreover, changes in the socio-cultural environment in India necessitate that adolescents’ influence in family’s buying decision-making should be investigated. In comparison to Western society, Indian is quite different, when compared in terms of family composition and structure, behaviour, values and norms which effect adolescents’ buying decision-making.

Keywords: adolescents, buying behavior, Indian urban families, consumer socialization

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Authors: Pascal François Faye, Baye Mor Sall, Bineta Dembele, Jeanne Ana Awa Faye

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In agriculture, estimating crop yields is key to improving productivity and decision-making processes such as financial market forecasting and addressing food security issues. The main objective of this paper is to have tools to predict and improve the accuracy of crop yield forecasts using machine learning (ML) algorithms such as CART , KNN and SVM . We developed a mobile app and a web app that uses these algorithms for practical use by farmers. The tests show that our system (collection and deployment architecture, web application and mobile application) is operational and validates empirical knowledge on agro-climatic parameters in addition to proactive decision-making support. The experimental results obtained on the agricultural data, the performance of the ML algorithms are compared using cross-validation in order to identify the most effective ones following the agricultural data. The proposed applications demonstrate that the proposed approach is effective in predicting crop yields and provides timely and accurate responses to farmers for decision support.

Keywords: prediction, machine learning, artificial intelligence, digital agriculture

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Authors: Nilubon Kurubanjerdjit, Nattakarn Iam-On, Ka-Lok Ng

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MicroRNAs are small non-coding RNA found in many different species. They play crucial roles in cancer such as biological processes of apoptosis and proliferation. The identification of microRNA-target genes can be an essential first step towards to reveal the role of microRNA in various cancer types. In this paper, we predict miRNA-target genes for lung cancer by integrating prediction scores from miRanda and PITA algorithms used as a feature vector of miRNA-target interaction. Then, machine-learning algorithms were implemented for making a final prediction. The approach developed in this study should be of value for future studies into understanding the role of miRNAs in molecular mechanisms enabling lung cancer formation.

Keywords: microRNA, miRNAs, lung cancer, machine learning, Naïve Bayes, SVM

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Authors: Juan Torralba

Abstract:

Student digital artifacts were investigated, utilizing a qualitative exploratory research design to understand the ways in which students represented their knowledge of seventh-grade proportionality concepts as they participated in maker-based activities that culminated in the creation of digital 3-dimensional models of their dream homes. Representations of the geometric and numeric dimensions of proportionality were analyzed in the written, verbal, and visual data collected from the students. A directed content analysis approach was utilized in the data analysis, as this work aimed to build upon existing research in the field of maker-based STEAM Education. The results from this work show that students can represent their understanding of proportional reasoning through open-ended written responses more accurately than through verbal descriptions or digital artifacts. The geometric and numeric dimensions of proportionality and their respective components of attributes of similarity representation and percents, rates, and ratios representations were the most represented by the students than any other across the data, suggesting a maker-based instructional approach to teaching proportionality in the middle grades may be promising in helping students gain a solid foundation in those components. Recommendations for practice and research are discussed.

Keywords: learning through making, maker-based education, maker education in the middle grades, making in mathematics, the maker movement

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Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

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Authors: O. Bourkoukou, Essaid El Bachari

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The group composing is one of the key issue in collaborative learning to achieve a positive educational experience. The goal of this work is to propose for teachers and tutors a method to create effective collaborative learning groups in e-learning environment based on the learner profile. For this purpose, a new function was defined to rate implicitly learning objects used by the learner during his learning experience. This paper describes the proposed algorithm to build an adequate collaborative learning group. In order to verify the performance of the proposed algorithm, several experiments were conducted in real data set in virtual environment. Results show the effectiveness of the method for which it appears that the proposed approach may be promising to produce better outcomes.

Keywords: building groups, collaborative learning, e-learning, learning objects

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Authors: Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui

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Recommender systems have become increasingly popular in recent years, and are utilized in numerous areas. Nowadays many web services provide several information for users and recommender systems have been developed as critical element of these web applications to predict choice of preference and provide significant recommendations. With the help of the advantage of deep learning in modeling different types of data and due to the dynamic change of user preference, building a deep model can better understand users demand and further improve quality of recommendation. In this paper, deep neural network models for recommender system are evaluated. Most of deep neural network models in recommender system focus on the classical collaborative filtering user-item setting. Deep learning models demonstrated high level features of complex data can be learned instead of using metadata which can significantly improve accuracy of recommendation. Even though deep learning poses a great impact in various areas, applying the model to a recommender system have not been fully exploited and still a lot of improvements can be done both in collaborative and content-based approach while considering different contextual factors.

Keywords: big data, decision making, deep learning, recommender system

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Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

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Authors: Yasaswi Palagummi, Sareh Rowlands

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Generalised Zero-Shot Learning, often known as GZSL, is an advanced variant of zero-shot learning in which the samples in the unseen category may be either seen or unseen. GZSL methods typically have a bias towards the seen classes because they learn a model to perform recognition for both the seen and unseen classes using data samples from the seen classes. This frequently leads to the misclassification of data from the unseen classes into the seen classes, making the task of GZSL more challenging. In this work of ours, to solve the GZSL problem, we propose an approach leveraging the Shifted Window based Self-Attention in the Swin Transformer (Swin-GZSL) to work in the inductive GSZL problem setting. We run experiments on three popular benchmark datasets: CUB, SUN, and AWA2, which are specifically used for ZSL and its other variants. The results show that our model based on Swin Transformer has achieved state-of-the-art harmonic mean for two datasets -AWA2 and SUN and near-state-of-the-art for the other dataset - CUB. More importantly, this technique has a linear computational complexity, which reduces training time significantly. We have also observed less bias than most of the existing GZSL models.

Keywords: generalised, zero-shot learning, inductive learning, shifted-window attention, Swin transformer, vision transformer

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Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

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Authors: Sulaiman Almutairy, Trevor Davies, Yota Dimitriadi

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This paper presents study investigating how to understand better the psychological readiness for mobile learning (m-learning) among Saudi students, while also evaluating m-learning in Saudi Arabia-a topic that has not yet received adequate attention from researchers. Data was acquired through a questionnaire administered to 131 Saudi students at UK universities, in July 2013. The study confirmed that students are confident using mobile devices in their daily lives and that they would welcome more opportunities for mobile learning. The findings indicated that Saudi higher education students are highly familiar with, and are psychologically ready for, m-learning.

Keywords: m-learning, mobile technologies, psychological readiness, higher education

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Authors: M. Rodionov, Z. Dedovets

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This article discusses ways to implement a differentiated approach to developing academic motivation for mathematical studies which relies on defining the primary structural characteristics of motivation. The following characteristics are considered: features of realization of cognitive activity, meaning-making characteristics, level of generalization and consistency of knowledge acquired by personal experience. The assessment of the present level of individual student understanding of each component of academic motivation is the basis for defining the relevant educational strategy for its further development.

Keywords: learning activity, mathematics, motivation, student

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Authors: Chryssi Vitsilaki, Apostolos Kostas, Ilias Efthymiou

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This paper presents selected best practices on online learning and teaching derived from a novel and innovating Lifelong Learning program through e-Learning, which has during the last five years been set up at the University of the Aegean in Greece. The university, capitalizing on an award-winning, decade-long experience in e-learning and blended learning in undergraduate and postgraduate studies, recently expanded into continuous education and vocational training programs in various cutting-edge fields. So, in this article we present: (a) the academic structure/infrastructure which has been developed for the administrative, organizational and educational support of the e-Learning process, including training the trainers, (b) the mode of design and implementation based on a sound pedagogical framework of open and distance education, and (c) the key results of the assessment of the e-learning process by the participants, as they are used to feedback on continuous organizational and teaching improvement and quality control.

Keywords: distance education, e-learning, life-long programs, synchronous/asynchronous learning

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Authors: Albert T. Akume, P. D. Dahida

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This paper examines the effect of various epochs of governments on policy making in Nigeria. The character of governance and public policy making of both epochs was exclusive, non-participatory and self-centric. As a consequence the interests of citizenry were not represented, neither protected nor sought to meet fairly the needs of all groups. The introduction of the post-1999 democratic government demand that the hitherto skewed pattern of policy making cease to be a character of governance. Hence, the need for citizen participation in the policy making process. The question then is what mode is most appropriate to engender public participation so as to make the policy making process functional? Given the prevailing social, economic and political dilemmas the utilization of the direct mode of citizen participation to affect policy outcome is doubtful if not unattainable. It is due to these predicament that this paper uses the documentary research design argues for the utilization of the indirect mode of citizen participation in the policy making process so as to affect public policy outcome appropriately and with less cost, acrimony and delays.

Keywords: governance, public policy, participation, representation, civil society

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Authors: Zhou Hong, Gu Xiao-Qing, Lıu Hong-Jiao, Leng Jing

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Seamless learning is becoming a research hotspot in recent years, and the emerging of micro-lectures, flipped classroom has strengthened the development of seamless learning. Based on the characteristics of the seamless learning across time and space and the course structure of the flipped classroom, and the theories of language learning, we put forward the language learning model which can support ‘seamless flipped’ environment (abbreviated as ‘S-F’). Meanwhile, the characteristics of the ‘S-F’ learning environment, the corresponding framework construction and the activity design of diversified corpora were introduced. Moreover, a language learning app named ‘Daily Speaking’ was developed to facilitate the practice of the language learning model in ‘S-F’ environment. In virtue of the learning case of Shanghai language, the rationality and feasibility of this framework were examined, expecting to provide a reference for the design of ‘S-F’ learning in different situations.

Keywords: seamless learning, flipped classroom, seamless-flipped environment, language learning model

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Authors: Shoba Bandi-Rao

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The lack of college readiness is one of the major contributors to low graduation rates at community colleges, especially among educationally and financially disadvantaged students. About 45% of underprepared high school graduates are required to complete ‘remedial’ reading/writing courses before they can begin taking college-level courses. Mobile apps present ‘bite-size’ learning materials that can be useful for practicing certain literacy skills, such as vocabulary learning. The convenience of mobile phones is ideal for a majority of students at community colleges who hold full or part-time jobs. Mobile apps allow students to learn during small ‘chunks’ of time available to them outside of the class—during subway commute, between classes, etc. Learning with mobile apps is a relatively new area in research, and their effectiveness for learning new words has been inconclusive. Using Mishra & Koehler’s TPCK theoretical framework, this study explored the effectiveness of the mobile app (Quizlet) for learning one hundred common college-level words in ‘remedial’ writing class over one semester. Each week, before coming to class, students studied a list of 10-15 words presented in context within sentences. Students came across these words in the article they read in class making their learning more meaningful. A pre and post-test measured the number of words students knew, learned and remembered. Statistical analysis shows that students performed better by 41% on the post-test indicating that the mobile app was helpful for learning words. Students also completed a short survey each week that sought to determine the amount of time students spent on the vocabulary app. A positive correlation was found between the amount of time spent on the mobile app and the number of words learned. The goal of this research is to capitalize on the convenience of smartphones to (1) better prepare them for college-level course work, and (2) contribute to current literature on mobile learning.

Keywords: mobile learning, vocabulary learning, literacy skills, Quizlet

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Authors: Marina Tsetseli, Eleni Zetou, Maria Michalopoulou, Nikos Vernadakis

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Game performance, besides the accuracy and the quality skills execution, depends heavily on where the athletes will focus their attention while performing a skill. The purpose of the present study was to examine and compare the effect of internal and external focus of attention instructions on the decision making in tennis at players 8-9 years old (M=8.4, SD=0.49). The participants (N=40) were divided into two groups and followed an intervention training program that lasted 4 weeks; first group (N=20) under internal focus of attention instructions and the second group (N=20) under external focus of attention instructions. Three measurements took place (pre-test, post-test, and retention test) in which the participants were video recorded while playing matches in real scoring conditions. GPAI (Game Performance Assessment Instrument) was used to evaluate decision making in three game situations; service, return of the service, baseline game. ANOVA repeated measures (2 groups x 3 measurements) revealed a significant interaction between groups and measurements. Specifically, the data analysis showed superiority of the group that was instructed to focus externally. The high scores of the external attention group were maintained at the same level at the third measurement as well, which indicates that the impact was concerning not only performance but also learning. Thus, cues that lead to an external focus of attention enhance the decision-making skill and therefore the game performance of the young tennis players.

Keywords: decision making, evaluation, focus of attention, game performance, tennis

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Authors: Albin Wallace

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This paper examines the use of social learning platforms in conjunction with the emergent pedagogy of the ‘flipped classroom’. In particular the attributes of the social learning platform “Edmodo” is considered alongside the changes in the way in which online learning environments are being implemented, especially within British education. Some observations are made regarding the use and usefulness of these platforms along with a consideration of the increasingly decentralized nature of education in the United Kingdom.

Keywords: education, Edmodo, Internet, learning platforms

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Authors: Mehwish Raza

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Mobile learning (m-learning) offers unique affordances to learners, setting them free of limitations posed by time and geographic space; thus becoming an affordable device for convenient distant learning. There is a plethora of research available on mobile learning projects planned, implemented and evaluated across disciplines in the context of developed countries, however, the potential of m-learning at different educational levels remain unexplored with little evidence of research carried out in developing countries. Despite the favorable technical infrastructure offered by cellular networks and boom in mobile subscriptions in the developing world, there is limited focus on utilizing m-learning for education and development purposes. The objective of this review is to unify findings from m-learning projects that have been implemented in developing countries such as Pakistan, Bangladesh, Philippines, India, and Tanzania for teachers’ in-service training. The purpose is to draw upon key characteristics of mobile learning that would be useful for future researchers to inform conceptualizations of mobile learning for developing countries.

Keywords: design model, developing countries, key characteristics, mobile learning

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Authors: Razzaghifard P., Arya F., Chen S. Chien-I, Abdi B., Razzaghifard V., Arya A. H., Nazary A., Hosseinpour H., Ghabelnezam K.

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E-learning, also known as online learning, has indicated increased growth in recent years. One of the critical factors in the successful application of e-learning in higher education is students’ perceptions towards it. The main purpose of this paper is to investigate the perceptions of engineering students about e-learning in the UK. For the purpose of the present study, 145 second-year engineering students were randomly selected from the total population of 1280 participants. The participants were asked to complete a questionnaire containing 16 items. The data collected from the questionnaire were analyzed through the Statistical Package for Social Science (SPSS) software. The findings of the study revealed that the majority of participants have negative perceptions of e-learning. Most of the students had trouble interacting effectively during online classes. Furthermore, the majority of participants had negative experiences with the learning platform they used during e-learning. Suggestions were made on what could be done to improve the students’ perceptions of e-learning.

Keywords: e-learning, higher, education, engineering education, online learning

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Authors: Adeniran Adetunji, Babalola M. Florence, Akande Ademola

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In this paper, we made an overview on the concept of adaptive e-Learning system, enumerates the elements of adaptive learning concepts e.g. A pedagogical framework, multiple learning strategies and pathways, continuous monitoring and feedback on student performance, statistical inference to reach final learning strategy that works for an individual learner by “mass-customization”. Briefly highlights the motivation of this new system proposed for effective learning teaching. E-Review literature on the concept of adaptive e-learning system and emphasises on the Item Response Calibration, which is an important approach to developing an adaptive e-Learning system. This paper write-up is concluded on the justification of item response calibration/estimation towards designing a successful and effective adaptive e-Learning system.

Keywords: adaptive e-learning system, pedagogical framework, item response, computer applications

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Authors: Luo Jiaoyang, Yu Hongyang

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In this paper, we explore a method that can be used in the working scene of intelligent industrial robots to confirm the identity information of operators to ensure that the robot executes instructions in a sufficiently safe environment. This approach uses three information modalities, namely visible light, depth, and sound. We explored a variety of fusion modes for the three modalities and finally used the joint feature learning method to improve the performance of the model in the case of noise compared with the single-modal case, making the maximum noise in the experiment. It can also maintain an accuracy rate of more than 90%.

Keywords: multimodal, kinect, machine learning, distance image

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Authors: Massra M. Sabeima, Myriam lamolle, Mohamedade Farouk Nanne

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Adapting the learning resources of an e-learning system to the characteristics of the learners is an important aspect to consider when designing an adaptive e-learning system. However, this adaptation is not a simple process; it requires the extraction, analysis, and modeling of user information. This implies a good representation of the user's profile, which is the backbone of the adaptation process. Moreover, during the e-learning process, collaboration with similar users (same geographic province or knowledge context) is important. Productive collaboration motivates users to continue or not abandon the course and increases the assimilation of learning objects. The contribution of this work is the following: we propose an adaptive e-learning semantic platform to recommend learning resources to learners, using ontology to model the user profile and the course content, furthermore an implementation of a multi-agent system able to progressively generate the learning graph (taking into account the user's progress, and the changes that occur) for each user during the learning process, and to synchronize the users who collaborate on a learning object.

Keywords: adaptative learning, collaboration, multi-agent, ontology

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Authors: Ghiwa Dandach

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By challenging the anti-failure bias and contributing to the theoretical territory of the attribution theory, this thesis develops a comprehensive process for entrepreneurial learning from failure. The practical implication of the findings suggests assisting entrepreneurs (current, failing, and nascent) in effectively anticipating and reflecting upon failure. Additionally, the process is suggested to enhance the level of institutional and private (accelerators and financers) support provided to entrepreneurs, the implications of which may improve future opportunities for entrepreneurial success. Henceforth, exploring learning from failure is argued to impact the potential survival of future ventures, subsequently revitalizing the economic contribution of entrepreneurship. This learning process can be enhanced with the cognitive development of causal ascriptions for failure, which eventually impacts learning outcomes. However, the mechanism with which entrepreneurs make sense of failure, reflect on the journey, and transform experience into knowledge is still under-researched. More specifically, the cognitive process of failure attribution is under-explored, majorly in the context of developing economies, calling for a more insightful understanding on how entrepreneurs ascribe failure. Responding to the call for more thorough research in such cultural contexts, this study expands the understanding of the dimensions of failure attributions as perceived by entrepreneurs and the impact of these dimensions on learning outcomes in the Lebanese context. The research adopted the exploratory interpretivism paradigm and collected data from interviews with industry experts first, followed by narratives of entrepreneurs using the qualitative multimethod approach. The holistic and categorical content analysis of narratives, preceded by the thematic analysis of interviews, unveiled how entrepreneurs ascribe failure by developing minor and major dimensions of each failure attribution. The findings have also revealed how each dimension impacts the learning from failure when accompanied by emotional resilience. The thesis concludes that exploring in-depth the dimensions of failure attributions significantly determines the level of learning generated. They are moving beyond the simple categorisation of ascriptions as primary internal or external unveiled how learning may occur with each attribution at the individual, venture, and ecosystem levels. This has further accentuated that a major internal attribution of failure combined with a minor external attribution generated the highest levels of transformative and double-loop learning, emphasizing the role of personal blame and responsibility on enhancing learning outcomes.

Keywords: attribution, entrepreneurship, reflection, sense-making, emotions, learning outcomes, failure, exit

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Authors: Paduri Veerabhadram, Antoinette Lombard

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In this paper a framework for hypothesizing about mobile learning to complement theories of formal and informal learning is presented. As such, activity theory will form the main theoretical lens through which the elements involved in formal and informal learning for mobile learning will be explored, specifically related to context-aware mobile learning application. The author believes that the complexity of the relationships involved can best be analysed using activity theory. Activity theory, as a social, cultural and activity theory can be used as a mobile learning framework in an academic environment, but to develop an optimal artifact, through investigation of inherent system's contradictions. As such, it serves as a powerful modelling tool to explore and understand the design of a mobile learning environment in the study’s environment. The Academic Tool Kit Framework (ATKF) as also employed for designing of a constructivism learning environment, effective in assisting universities to facilitate lecturers to effectively implement learning through utilizing mobile devices. Results indicate a positive perspective of students in the use of mobile devices for formal and informal learning, based on the context-aware learning environment developed through the use of activity theory and ATKF.

Keywords: collaborative learning, cooperative learning, context-aware learning environment, mobile learning, pedagogy

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Authors: Yasir Naseem

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In this fast-growing and evolving world, usage and adoption of technology have displayed an essential component of the learning process, both in and out of the class, which converges and incorporates every domain of the learning aspects. It aids in learning distinct entities irrespective of their levels of challenge. It also incorporates both viewpoints of learning, i.e., competence as well as the performances of the learner. In today's learning scenario, nearly every language class ordinarily uses some form of technology. It integrates with various teaching methodologies and transforms in a way that now it grew as an integral part of the language learning courses. It has been employed to facilitate, promote, and enhances language learning. It facilitates educators in numerous ways and enhances their methodologies by equipping them to modify classroom activities, which covers every aspect of language learning.

Keywords: communication, methodology, technology, skills

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Authors: Muhalim Muhalim

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Drawing on Lakoff’s theory of metaphor conceptualization, this article explores the conceptualization of language two writing (L2W) of ten students-teachers in Indonesia via metaphors. The ten postgraduate English language teaching students and at the same time (former) English teachers received seven days of intervention in teaching and learning L2. Using introspective log and focus group discussion, the results illuminate us that all participants are unanimous on perceiving L2W as process-oriented rather than product-oriented activity. Specifically, the metaphor conceptualizations exhibit three categories of process-oriented L2W: deliberate process, learning process, and problem-solving process. However, it has to be clarified from the outset that this categorization is not rigid because some of the properties of metaphors might belong to other categories. Results of the study and implications for English language teaching will be further discussed.

Keywords: metaphor conceptualisation, second language, learning writing, teaching writing

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Authors: Olteanu Constanta, Olteanu Lucian

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Researchers note that algebraic reasoning and sense making is essential for building conceptual knowledge in school mathematics. Consequently, pre-service teachers’ own reasoning and sense making are useful in fostering and developing students’ algebraic reasoning and sense making. This article explores the forms of reasoning and sense making that pre-service mathematics teachers exhibit and use in the process of analysing problem-posing tasks with a focus on first-degree equations. Our research question concerns the characteristics of the problem-posing tasks used for reasoning and sense making of first-degree equations as well as the characteristics of pre-service teachers’ reasoning and sense making in problem-posing tasks. The analyses are grounded in a post-structuralist philosophical perspective and variation theory. Sixty-six pre-service primary teachers participated in the study. The results show that the characteristics of reasoning in problem-posing tasks and of pre-service teachers are selecting, exploring, reconfiguring, encoding, abstracting and connecting. The characteristics of sense making in problem-posing tasks and of pre-service teachers are recognition, relationships, profiling, comparing, laddering and verifying. Beside this, the connection between reasoning and sense making is rich in line of flight in problem-posing tasks, while the connection is rich in line of rupture for pre-service teachers.

Keywords: first-degree equations, problem posing, reasoning, rhizomatic assemblage, sense-making, variation theory

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Authors: Chinlun Lai, Yu-Mei Chang

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In this paper, an augmented reality learning support system is proposed to replace the traditional teaching tool thus to help students improve their learning motivation, effectiveness, and efficiency. The system can not only reduce the exhaust of educational hardware and realistic material, but also provide an eco-friendly and self-learning practical environment in any time and anywhere with immediate practical experiences feedback. To achieve this, an interactive self-training methodology which containing step by step operation directions is designed using virtual 3D scenario and wearable device platforms. The course of nasogastric tube care of nursing skills is selected as the test example for self-learning and online test. From the experimental results, it is observed that the support system can not only increase the student’s learning interest but also improve the learning performance than the traditional teaching methods. Thus, it fulfills the strategy of learning by practice while reducing the related cost and effort significantly and is practical in various fields.

Keywords: augmented reality technology, learning support system, self-learning, simulation learning method

Procedia PDF Downloads 142