Search results for: Experience based learning

Authors: Helle Hein, Ülo Lepik

Abstract:

A gradient learning method to regulate the trajectories of some nonlinear chaotic systems is proposed. The method is motivated by the gradient descent learning algorithms for neural networks. It is based on two systems: dynamic optimization system and system for finding sensitivities. Numerical results of several examples are presented, which convincingly illustrate the efficiency of the method.

Keywords: Chaos, Dynamical Systems, Learning, Neural Networks

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Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

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Authors: R. Ramli, S. Sameon

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There has been a strong link between computermediated education and constructivism learning and teaching theory.. Acknowledging how well the constructivism doctrine would work online, it has been established that constructivist views of learning would agreeably correlate with the philosophy of open and distance learning. Asynchronous and synchronous communications have placed online learning on the right track of a constructive learning path. This paper is written based on the social constructivist framework, where knowledge is constructed from social communication and interaction. The study explores the possibility of practicing this theory through incorporating online discussion in the syllabus and the ways it can be implemented to contribute to young people-s personality and social development by addressing some aspects that may contribute to the social problem such as prejudice, ignorance and intolerance.

Keywords: Educational Technology, Internet, Personal Development, Student Exchange

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Authors: Muhammed Yousoof, Mohd Sapiyan, Khaja Kamaluddin

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Learning programming is difficult for many learners. Some researches have found that the main difficulty relates to cognitive load. Cognitive overload happens in programming due to the nature of the subject which is intrinisicly over-bearing on the working memory. It happens due to the complexity of the subject itself. The problem is made worse by the poor instructional design methodology used in the teaching and learning process. Various efforts have been proposed to reduce the cognitive load, e.g. visualization softwares, part-program method etc. Use of many computer based systems have also been tried to tackle the problem. However, little success has been made to alleviate the problem. More has to be done to overcome this hurdle. This research attempts at understanding how cognitive load can be managed so as to reduce the problem of overloading. We propose a mechanism to measure the cognitive load during pre instruction, post instruction and in instructional stages of learning. This mechanism is used to help the instruction. As the load changes the instruction is made to adapt itself to ensure cognitive viability. This mechanism could be incorporated as a sub domain in the student model of various computer based instructional systems to facilitate the learning of programming.

Keywords: Cognitive load, Working memory, Cognitive Loadmeasurement.

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Authors: A.Ö. İlkay, C.O. Zeynep

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Information and Communication Technologies (ICT) has changed our life and learn. ICT bares doors to new innovative methods to deliver education. E-learning is a part of ICT and has been endorsed as a tool for developing “21st century skills” in higher education. The aim of this review is to establish the impacts of e-learning in undergraduate nursing education. A systematic literature review was conducted to assess the impacts of e-learning in nursing education by using Akdeniz University electronic databases. According to results, we can decelerate that the nursing faculties cannot treat e-learning methods as a single tool. E-learning should be used with a good understanding of learners’ needs.

Keywords: E-learning, nursing education, systematic literature review.

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Authors: John Tarus, Zhendong Niu, Bakhti Khadidja

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In recent years, e-learning recommender systems has attracted great attention as a solution towards addressing the problem of information overload in e-learning environments and providing relevant recommendations to online learners. E-learning recommenders continue to play an increasing educational role in aiding learners to find appropriate learning materials to support the achievement of their learning goals. Although general recommender systems have recorded significant success in solving the problem of information overload in e-commerce domains and providing accurate recommendations, e-learning recommender systems on the other hand still face some issues arising from differences in learner characteristics such as learning style, skill level and study level. Conventional recommendation techniques such as collaborative filtering and content-based deal with only two types of entities namely users and items with their ratings. These conventional recommender systems do not take into account the learner characteristics in their recommendation process. Therefore, conventional recommendation techniques cannot make accurate and personalized recommendations in e-learning environment. In this paper, we propose a recommendation technique combining collaborative filtering and ontology to recommend personalized learning materials to online learners. Ontology is used to incorporate the learner characteristics into the recommendation process alongside the ratings while collaborate filtering predicts ratings and generate recommendations. Furthermore, ontological knowledge is used by the recommender system at the initial stages in the absence of ratings to alleviate the cold-start problem. Evaluation results show that our proposed recommendation technique outperforms collaborative filtering on its own in terms of personalization and recommendation accuracy.

Keywords: Collaborative filtering, e-learning, ontology, recommender system.

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Authors: M. Zaitoun, S. Cumming, A. Purcell

Abstract:

This study aims to determine if audiologists' experience characteristics in ABR (Auditory Brainstem Response) reading is associated with their performance in interpreting ABR results. Fifteen ABR traces with varying degrees of hearing level were presented twice, making a total of 30. Audiologists were asked to determine the hearing threshold for each of the cases after completing a brief survey regarding their experience and training in ABR administration. Sixty-one audiologists completed all tasks. Correlations between audiologists’ performance measures and experience variables suggested significant associations (p < 0.05) between training period in ABR testing and audiologists’ performance in terms of both sensitivity and accuracy. In addition, the number of years conducting ABR testing correlated with specificity. No other correlations approached significance. While there are relatively few significant correlations between ABR performance and experience, accuracy in ABR reading is associated with audiologists’ length of experience and period of training. To improve audiologists’ performance in reading ABR results, an emphasis on the importance of training should be raised and standardized levels and period for audiologists training in ABR testing should also be set.

Keywords: ABR, audiology, performance, training, experience.

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Authors: Chalise Kiran

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The learning difficulties of children with disabilities are always a matter of concern when we talk about educational needs and quality education of children with disabilities. This paper is the outcome of the review of the literature focused on the educational needs and learning difficulties of children with disabilities. For the paper, different studies written on children with disabilities and their education were collected through search engines. The literature put together were analyzed from the angle of learning difficulties faced by children with disabilities and the same were used as a precursor to arrive at the findings on the learning of the children. The analysis showed that children with disabilities face learning difficulties. The reasons for these difficulties could be attributed to factors in terms of authority, structure, school environment and behaviors of teachers and parents and the society as a whole.

Keywords: Children with disabilities, learning difficulties, education of children with disabilities, disabled children.

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Authors: Hadi Firouzi, Majid Nili Ahmadabadi, Babak N. Araabi

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Conceptualization strengthens intelligent systems in generalization skill, effective knowledge representation, real-time inference, and managing uncertain and indefinite situations in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction by which the continuous state is formed as entities called concepts which are connected to the action space and thus, they illustrate somehow the complex action space. Of computational concept learning approaches, action-based conceptualization is favored because of its simplicity and mirror neuron foundations in neuroscience. In this paper, a new biologically inspired concept learning approach based on the probabilistic framework is proposed. This approach exploits and extends the mirror neuron-s role in conceptualization for a reinforcement learning agent in nondeterministic environments. In the proposed method, instead of building a huge numerical knowledge, the concepts are learnt gradually from rewards through interaction with the environment. Moreover the probabilistic formation of the concepts is employed to deal with uncertain and dynamic nature of real problems in addition to the ability of generalization. These characteristics as a whole distinguish the proposed learning algorithm from both a pure classification algorithm and typical reinforcement learning. Simulation results show advantages of the proposed framework in terms of convergence speed as well as generalization and asymptotic behavior because of utilizing both success and failures attempts through received rewards. Experimental results, on the other hand, show the applicability and effectiveness of the proposed method in continuous and noisy environments for a real robotic task such as maze as well as the benefits of implementing an incremental learning scenario in artificial agents.

Keywords: Concept learning, probabilistic decision making, reinforcement learning.

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Authors: S. Sarumathi, M. Vaishnavi, S. Geetha, P. Ranjetha

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Machine learning is a new and exciting area of artificial intelligence nowadays. Machine learning is the most valuable, time, supervised, and cost-effective approach. It is not a narrow learning approach; it also includes a wide range of methods and techniques that can be applied to a wide range of complex realworld problems and time domains. Biological image classification, adaptive testing, computer vision, natural language processing, object detection, cancer detection, face recognition, handwriting recognition, speech recognition, and many other applications of machine learning are widely used in research, industry, and government. Every day, more data are generated, and conventional machine learning techniques are becoming obsolete as users move to distributed and real-time operations. By providing fundamental knowledge of machine learning tools and research opportunities in the field, the aim of this article is to serve as both a comprehensive overview and a guide. A diverse set of machine learning resources is demonstrated and contrasted with the key features in this survey.

Keywords: Artificial intelligence, machine learning, deep learning, machine learning algorithms, machine learning tools.

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Authors: Leon Pan

Abstract:

The first programming course within post-secondary education has long been recognized as a challenging endeavor for both educators and students alike. Historically, these courses have exhibited high failure rates and a notable number of dropouts. Instructors often lament students' lack of effort on their coursework, and students often express frustration that the teaching methods employed are not effective. Drawing inspiration from the successful principles of Extreme Programming, this study introduces an approach—the Extremes-based teaching model—aimed at enhancing the teaching of introductory programming courses. To empirically determine the effectiveness of the model, a comparison was made between a section taught using the extreme-based model and another utilizing traditional teaching methods. Notably, the extreme-based teaching class required students to work collaboratively on projects, while also demanding continuous assessment and performance enhancement within groups. This paper details the application of the extreme-based model within the post-secondary online classroom context and presents the compelling results that emphasize its effectiveness in advancing the teaching and learning experiences. The extreme-based model led to a significant increase of 13.46 points in the weighted total average and a commendable 10% reduction in the failure rate.

Keywords: Extreme-based teaching model, innovative pedagogical methods, project-based learning, team-based learning.

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Authors: Varun Gupta, Ankit Pandey

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Crude oil market is an immensely complex and dynamic environment and thus the task of predicting changes in such an environment becomes challenging with regards to its accuracy. A number of approaches have been adopted to take on that challenge and machine learning has been at the core in many of them. There are plenty of examples of algorithms based on machine learning yielding satisfactory results for such type of prediction. In this paper, we have tried to predict crude oil prices using Long Short-Term Memory (LSTM) based recurrent neural networks. We have tried to experiment with different types of models using different epochs, lookbacks and other tuning methods. The results obtained are promising and presented a reasonably accurate prediction for the price of crude oil in near future.

Keywords: Crude oil price prediction, deep learning, LSTM, recurrent neural networks.

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Authors: YuLung Wu

Abstract:

In Knowledge Structure Graph, each course unit represents a phase of learning activities. Both learning portfolios and Knowledge Structure Graphs contain learning information of students and let teachers know which content are difficulties and fails. The study purposes "Dual Mode On-line Learning Diagnosis System" that integrates two search methods: learning portfolio and knowledge structure. Teachers can operate the proposed system and obtain the information of specific students without any computer science background. The teachers can find out failed students in advance and provide remedial learning resources.

Keywords: Knowledge Structure Graph, On-line LearningDiagnosis

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Authors: Nikolina Ribarić

Abstract:

E-learning is most commonly defined as a set of applications and processes, such as Web-based learning, computer-based learning, virtual classrooms and digital collaboration, that enable access to instructional content through a variety of electronic media. The main goal of an e-learning system is learning, and the way to evaluate the impact of an e-learning system is by examining whether students learn effectively with the help of that system. Testmoz is a program for online preparation of knowledge evaluation assignments. The program provides teachers with computer support during the design of assignments and evaluating them. Students can review and solve assignments and also check the correctness of their solutions. Research into the increase of motivation by the practice of providing teaching content by applying online tests prepared in the Testmoz program, was carried out with students of the 8th grade of Ljubo Babić Primary School in Jastrebarsko. The students took the tests in their free time, from home, for an unlimited number of times. SPSS was used to process the data obtained by the research instruments. The results of the research showed that students preferred to practice teaching content, and achieved better educational results in chemistry, when they had access to online tests for repetition and practicing in relation to subject content which was checked after repetition and practicing in "the classical way" – i.e., solving assignments in a workbook or writing assignments in worksheets.

Keywords: Chemistry class, e-learning, online test, Testmoz.

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Authors: S. Sowmyayani

Abstract:

The advancement of computer-aided design helps the medical force and security force. Some applications include biometric recognition, elderly fall detection, face recognition, cancer recognition, tumor recognition, etc. This paper deals with different machine learning algorithms that are more generically used for any health care system. The most focused problems are classification and regression. With the rise of big data, machine learning has become particularly important for solving problems. Machine learning uses two types of techniques: supervised learning and unsupervised learning. The former trains a model on known input and output data and predicts future outputs. Classification and regression are supervised learning techniques. Unsupervised learning finds hidden patterns in input data. Clustering is one such unsupervised learning technique. The above-mentioned models are discussed briefly in this paper.

Keywords: Supervised learning, unsupervised learning, regression, neural network.

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Authors: G. Kumaravelan, R. Sivakumar

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Modeling the behavior of the dialogue management in the design of a spoken dialogue system using statistical methodologies is currently a growing research area. This paper presents a work on developing an adaptive learning approach to optimize dialogue strategy. At the core of our system is a method formalizing dialogue management as a sequential decision making under uncertainty whose underlying probabilistic structure has a Markov Chain. Researchers have mostly focused on model-free algorithms for automating the design of dialogue management using machine learning techniques such as reinforcement learning. But in model-free algorithms there exist a dilemma in engaging the type of exploration versus exploitation. Hence we present a model-based online policy learning algorithm using interconnected learning automata for optimizing dialogue strategy. The proposed algorithm is capable of deriving an optimal policy that prescribes what action should be taken in various states of conversation so as to maximize the expected total reward to attain the goal and incorporates good exploration and exploitation in its updates to improve the naturalness of humancomputer interaction. We test the proposed approach using the most sophisticated evaluation framework PARADISE for accessing to the railway information system.

Keywords: Dialogue management, Learning automata, Reinforcement learning, Spoken dialogue system

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Authors: Motlhale Judicial Sebatana, Washington Takawira Dudu

Abstract:

This study investigated how one beginner Physical Sciences teacher implemented Problem-Based Learning (PBL) strategy in the teaching and learning of Particulate Nature of Matter (PNM) in the Grade 10 classroom. PBL was implemented to explore how it can promote a 21st-century skill of creativity and enhance understanding of PNM. This study was guided by theoretical framework of Social Interdependence Theory (SIT). This exploratory qualitative case study was conveniently conducted in the North West province, South Africa, where one Physical Sciences teacher was purposefully sampled. A self-developed open-ended questionnaire, portfolio and individual semi-structured interview were used as the methods of generating data for this study. The results show that the participant of this study had no prior knowledge of utilising PBL in the teaching and learning of PNM before the Teacher Professional Development (TPD) programme, no knowledge of creativity as a 21st-century skill, and a successful PBL implementation post TPD to promote creativity.

Keywords: Beginner teachers, physical sciences teachers, problem-based learning, 21st-century skills, creativity skill, particulate nature of matter.

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Authors: Ahmed T. Alahmar

Abstract:

There is an increasing trend toward student-centred interactive e-learning methods and students’ feedback is a valuable tool for improving learning methods. The aim of this study was to explore the attitude of second year pharmacy students at the University of Babylon, Iraq, towards lectures, exams and e-learning. Materials and methods: Ninety pharmacy students were surveyed by paper questionnaire about their preference for lecture format, use of e-files, theoretical lectures versus practical experiments, lecture and lab time. Students were also asked about their predilection for Moodle-based online exams, different types of exam questions, exam time and other extra academic activities. Results: Students prefer to read lectures on paper (73.3%), use of PowerPoint file (76.7%), short lectures of less than 10 pages (94.5%), practical experiments (66.7%), lectures and lab time of less than two hours (89.9% and 96.6 respectively) and intra-lecture discussions (68.9%). Students also like to have paper-based exam (73.3%), short essay (40%) or MCQ (34.4%) questions and also prefer to do extra activities like reports (22.2%), seminars (18.6%) and posters (10.8%). Conclusion: Second year pharmacy students have different attitudes toward traditional and electronic leaning and assessment methods. Using multimedia, e-learning and Moodle are increasingly preferred methods among some students.

Keywords: Pharmacy, students, lecture, exam, e-learning, Moodle.

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Authors: Yu-Chin Kuo, Szu-Wei Yang, Hsin-Hung Kuo

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A Reading Comprehend (RC) Platform has been constructed and developed to facilitate children-s English reading comprehension. Like a learning bridge, the RC Platform focuses on the integration of rich media and picture-book texts. The study is to examine the effects of the project within the RC Platform for children. Two classes of fourth graders were selected from a public elementary school in an urban area of central Taiwan. The findings taken from the survey showed that the students demonstrated high interest in the RC Platform. The students benefited greatly and enjoyed reading via the technology-enhanced project within the RC Platform. This Platform is a good reading bridge to enrich students- learning experiences and enhance their performance in English reading comprehension.

Keywords: English Teaching, Multimedia-based Learning, Learning Platform, Reading Comprehension, Technology EnhancedLearning.

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Authors: Zoja Raud, Valery Vodovozov

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The paper focuses on the distance laboratory organisation for training the electrical engineering staff and students in the fields of electrical drive and power electronics. To support online knowledge acquisition and professional enhancement, new challenges in remote education based on an active learning approach with self-assessment have been emerged by the authors. Following the literature review and explanation of the improved assessment methodology, the concept and technological basis of the labs arrangement are presented. To decrease the gap between the distance study of the up-to-date equipment and other educational activities in electrical engineering, the improvements in the following-up the learners’ progress and feedback composition are introduced. An authoring methodology that helps to personalise knowledge acquisition and enlarge Web-based possibilities is described. Educational management based on self-assessment is discussed.

Keywords: Advanced training, active learning, distance learning, electrical engineering, remote laboratory, self-assessment.

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Authors: Matthew Montebello

Abstract:

The way e-learning effectiveness has been notoriously measured within an academic setting is by comparing the e-learning medium to the traditional face-to-face teaching methodology. In this paper, a simple yet innovative comparison methodology is introduced, whereby the effectiveness of next generation e-learning systems are assessed in contrast not only to the face-to-face mode, but also to the classical e-learning modality. Ethical and logistical issues are also discussed, as this three-way approach to compare teaching methodologies was applied and documented in a real empirical study within a higher education institution.

Keywords: E-learning effectiveness, higher education, teaching modality comparison.

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Authors: Wee Hock Quik, Nevan Wright

Abstract:

Collaborative networked learning (hereafter CNL) was first proposed by Charles Findley in his work “Collaborative networked learning: online facilitation and software support" as part of instructional learning for the future of the knowledge worker. His premise was that through electronic dialogue learners and experts could interactively communicate within a contextual framework to resolve problems, and/or to improve product or process knowledge. Collaborative learning has always been the forefront of educational technology and pedagogical research, but not in the mainstream of operations management. As a result, there is a large disparity in the study of CNL, and little is known about the antecedents of network collaboration and sharing of information among diverse employees in the manufacturing environment. This paper presents a model to bridge the gap between theory and practice. The objective is that manufacturing organizations will be able to accelerate organizational learning and sharing of information through various collaborative

Keywords: Collaborative networked learning, Collaborative technologies, Organizational learning, Synchronous and asynchronous networked learning.

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Authors: R. Boondao, A. J. Hurst, J. I. Sheard

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In the globalized e-learning environment, students coming from different cultures and countries have different characteristics and require different support designed for their approaches to study and learning styles. This paper explores the ways in which cultural background influences students- approaches to study and learning styles. Participants in the study consisted of 131 eastern students and 54 western students from an Australian university. The students were tested using the Study Process Questionnaire (SPQ) for assessing their approaches to study and the Index of Learning Styles Questionnaire (ILS) for assessing their learning styles. The results of the study led to a set of principles being proposed to guide personalization of e-learning system design on the basis of cultural differences.

Keywords: Approaches to study, Cultural influences, Learningstyles, Personalization, e-learning system.

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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. The research proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling. The paper concludes the challenges and improvement directions for Deep Reinforcement Learning-based resource scheduling algorithms.

Keywords: Resource scheduling, deep reinforcement learning, distributed system, artificial intelligence.

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Authors: Osama Abufanas

Abstract:

This paper aims to build an Arabic learning language tool using Flash CS4 professional software with action script 3.0 programming language, based on the Computer Aided Language Learning (CALL) material. An extra intention is to provide a primary tool and focus on learning Arabic as a second language to adults. It contains letters, words and sentences at the first stage. This includes interactive practices, which evaluates learners’ comprehension of the Arabic language. The system was examined and it was found that the language structure was correct and learners were satisfied regarding the system tools. The learners found the system tools efficient and simple to use. The paper's main conclusion illustrates that CALL can be applied without any hesitation to second language learners

Keywords: Arabic Language, Computer Aided Language Learning (CALL), Learner, Material.

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Authors: N. Khon, A. Kim, T. Mukhitdinova

Abstract:

The aim of this research was to reveal the link between mental variables, such as spatial abilities, memory, intellect and professional experience of drivers. Participants were allocated to four groups: no experience, inexperienced, skilled and professionals (total 85 participants). The level of ability for spatial navigation and indicator of nonverbal memory grow along the process of accumulation of driving experience. At high levels of driving experience, this tendency is especially noticeable. The professionals having personal achievements in driving (racing) differ from skilled drivers in better feeling of direction, which is specific for them not just in a short-term situation of an experimental task, but also in life-size perspective. The level of ability of mental rotation does not grow with the growth of driving experience, which confirms the multiple intelligence theory according to which spatial abilities represent specific, other than logical intelligence type of intellect. The link between spatial abilities, memory, intellect and professional experience of drivers seems to be different relating spatial navigation or mental rotation as different kinds of spatial abilities.

Keywords: Memory, spatial abilities, intellect, drivers.

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Authors: Thashmee Karunaratne, Henrik Böstrom

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Logic based methods for learning from structured data is limited w.r.t. handling large search spaces, preventing large-sized substructures from being considered by the resulting classifiers. A novel approach to learning from structured data is introduced that employs a structure transformation method, called finger printing, for addressing these limitations. The method, which generates features corresponding to arbitrarily complex substructures, is implemented in a system, called DIFFER. The method is demonstrated to perform comparably to an existing state-of-art method on some benchmark data sets without requiring restrictions on the search space. Furthermore, learning from the union of features generated by finger printing and the previous method outperforms learning from each individual set of features on all benchmark data sets, demonstrating the benefit of developing complementary, rather than competing, methods for structure classification.

Keywords: Machine learning, Structure classification, Propositionalization.

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Authors: Hesheng Wang, Haoyu Wang, Chungang Zhuang

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Estimating the 6D pose of objects is a core step for robot bin-picking tasks. The problem is that various objects are usually randomly stacked with heavy occlusion in real applications. In this work, we propose a method to regress 6D poses by predicting three points for each object in the 3D point cloud through deep learning. To solve the ambiguity of symmetric pose, we propose a labeling method to help the network converge better. Based on the predicted pose, an iterative method is employed for pose optimization. In real-world experiments, our method outperforms the classical approach in both precision and recall.

Keywords: Pose estimation, deep learning, point cloud, bin-picking, 3D computer vision.

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Authors: Tri Prasetyaningrum, Suyoto

Abstract:

In this paper, we will be present Guidance and Councelling (GC) class action research. The research was done because a fact that some students are still learning ways such as in elementary school. The research objective is to enhance the value of “academic performance report" grade by using ICT as GC Learning Guidance services. The research method was carried out with two cycles. First cycle is applying Learning Guidance services indirectly and not programmed. Second cycle into two implementing Learning Guidance services indirectly, programmed and using ICTs primarily mobile phones and computer media applications i.e. “m-NingBK©: Learning Guidance" and “screen saver: Learning Guidance". A research subject is a class VII student who has the lowest value of “academic performance report". The result is by using an indirect GC services with ICT there were significant changes.

Keywords: ICT, Learning Guidance, action research and Guidance and Councelling

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Authors: Babak Mohajeri, Sen Bao, Timo Nyberg

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Customer experience is increasingly the differentiator between successful companies and those who struggle. Currently, customer experiences become more dynamic; and they advance with each interaction between the company and a customer. Every customer conversation and any effort to evolve these conversations would be beneficial and should ultimately result in a positive customer experience. The aim of this paper is to analyze the evolving customer experience management landscape and the relevant challenges and opportunities. A case study on the “paper machine” companies is chosen. Hence, this paper analyzes the challenges and opportunities in customer experience management of paper machine companies for the case of “road to steel”. Road to steel shows the journey of steel from raw material to end product (i.e. paper machine in this paper). ALPHA (Steel company) and BETA (paper machine company), are chosen and their efforts to evolve the customer experiences are investigated. Semi-structured interviews are conducted with experts in those companies to identify the challenges and opportunities of the evolving customer experience management from their point of view. The findings of this paper contribute to the theory and business practices in the realm of the evolving customer experience management landscape.

Keywords: Customer experience management, paper machine risk analysis, value chain management.

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