Search results for: Cellular learning automata

Authors: Ferhat Erdal, Osman Tunca, Serkan Tas, Serdar Carbas

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Web-expanded steel beams provide an easy and economical solution for the systems having longer structural members. The main goal of manufacturing these beams is to increase the moment of inertia and section modulus, which results in greater strength and rigidity. Until recently, there were two common types of open web-expanded beams: with hexagonal openings, also called castellated beams, and beams with circular openings referred to as cellular beams, until the generation of sinusoidal web-expanded beams. In the present research, the optimum design of a new generation beams, namely sinusoidal web-expanded beams, will be carried out and the design results will be compared with castellated and cellular beam solutions. Thanks to a reduced fabrication process and substantial material savings, the web-expanded beam with sinusoidal holes (Angelina™ Beam) meets the economic requirements of steel design problems while ensuring optimum safety. The objective of this research is to carry out non-linear finite element analysis (FEA) of the web-expanded beam with sinusoidal holes. The FE method has been used to predict their entire response to increasing values of external loading until they lose their load carrying capacity. FE model of each specimen that is utilized in the experimental studies is carried out. These models are used to simulate the experimental work to verify of test results and to investigate the non-linear behavior of failure modes such as web-post buckling, shear buckling and vierendeel bending of beams.

Keywords: Steel structures, web-expanded beams, Angelina™ beam, optimum design, failure modes, finite element analysis.

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Authors: Ye Wei, Sitalakshmi Venkatraman, Fahri Benli, Fiona Wahr

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The present digital world poses many challenges to various stakeholders in the education sector. In particular, lecturers of higher education (HE) are faced with the problem of ensuring that students are able to achieve the required learning outcomes despite rapid changes taking place worldwide. Different strategies are adopted to retain student engagement and commitment in classrooms to address the differences in learning habits, preferences and styles of the digital generation of students recently. Further, with the onset of coronavirus disease (COVID-19) pandemic, online classroom has become the most suitable alternate mode of teaching environment to cope with lockdown restrictions. These changes have compounded the problems in the learning engagement and short attention span of HE students. New Agile methodologies that have been successfully employed to manage projects in different fields are gaining prominence in the education domain. In this paper, we present the application of Scrum as an agile methodology to enhance student learning and engagement in an online smart classroom environment. We demonstrate the use of our proposed approach using a case study to teach key topics in information technology that require students to gain technical and business-related data analytics skills.

Keywords: Agile methodology, Scrum, online learning, smart classroom environment, student engagement, active learning.

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Authors: Catalin Gales, Ionel Dumitrel Ghiba

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This paper studies questions of continuous data dependence and uniqueness for solutions of initial boundary value problems in linear micropolar thermoelastic mixtures. Logarithmic convexity arguments are used to establish results with no definiteness assumptions upon the internal energy.

Keywords: Cellular materials, continuous dependence, micro polar mixtures, uniqueness.

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Authors: Amal A. Abdel-Sattar

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The curriculum of architecture department in Prince Sultan University includes ‘Building Surveying’ course which is usually a part of civil engineering courses. As a fundamental requirement of the course, it requires a strong background in mathematics and physics, which are not usually preferred subjects to the architecture students and many of them are not giving the required and necessary attention to these courses during their preparation year before commencing their architectural study. This paper introduces the concept and the methodology of the student-centered learning approach in the course of building surveying for architects. One of the major outcomes is the improvement in the students’ involvement in the course and how this will cover and strength their analytical weak points and improve their mathematical skills. The study is conducted through three semesters with a total number of 99 students. The effectiveness of the student-centered learning approach is studied using the student survey at the end of each semester and teacher observations. This survey showed great acceptance of the students for these methods. Also, the teachers observed a great improvement in the students’ mathematical abilities and how keener they became in attending the classes which were clearly reflected on the low absence record.

Keywords: Architecture, building surveying, student-centered learning, teaching, and learning.

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Authors: János Levendovszky, András Oláh

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In this paper, novel statistical sampling based equalization techniques and CNN based detection are proposed to increase the spectral efficiency of multiuser communication systems over fading channels. Multiuser communication combined with selective fading can result in interferences which severely deteriorate the quality of service in wireless data transmission (e.g. CDMA in mobile communication). The paper introduces new equalization methods to combat interferences by minimizing the Bit Error Rate (BER) as a function of the equalizer coefficients. This provides higher performance than the traditional Minimum Mean Square Error equalization. Since the calculation of BER as a function of the equalizer coefficients is of exponential complexity, statistical sampling methods are proposed to approximate the gradient which yields fast equalization and superior performance to the traditional algorithms. Efficient estimation of the gradient is achieved by using stratified sampling and the Li-Silvester bounds. A simple mechanism is derived to identify the dominant samples in real-time, for the sake of efficient estimation. The equalizer weights are adapted recursively by minimizing the estimated BER. The near-optimal performance of the new algorithms is also demonstrated by extensive simulations. The paper has also developed a (Cellular Neural Network) CNN based approach to detection. In this case fast quadratic optimization has been carried out by t, whereas the task of equalizer is to ensure the required template structure (sparseness) for the CNN. The performance of the method has also been analyzed by simulations.

Keywords: Cellular Neural Network, channel equalization, communication over fading channels, multiuser communication, spectral efficiency, statistical sampling.

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Authors: Richard W. Longman

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Iterative learning control aims to achieve zero tracking error of a specific command. This is accomplished by iteratively adjusting the command given to a feedback control system, based on the tracking error observed in the previous iteration. One would like the iterations to converge to zero tracking error in spite of any error present in the model used to design the learning law. First, this need for stability robustness is discussed, and then the need for robustness of the property that the transients are well behaved. Methods of producing the needed robustness to parameter variations and to singular perturbations are presented. Then a method involving reverse time runs is given that lets the world behavior produce the ILC gains in such a way as to eliminate the need for a mathematical model. Since the real world is producing the gains, there is no issue of model error. Provided the world behaves linearly, the approach gives an ILC law with both stability robustness and good transient robustness, without the need to generate a model.

Keywords: Iterative learning control, stability robustness, monotonic convergence.

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Authors: Indiramma M., K. R. Anandakumar

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Trust management and Reputation models are becoming integral part of Internet based applications such as CSCW, E-commerce and Grid Computing. Also the trust dimension is a significant social structure and key to social relations within a collaborative community. Collaborative Decision Making (CDM) is a difficult task in the context of distributed environment (information across different geographical locations) and multidisciplinary decisions are involved such as Virtual Organization (VO). To aid team decision making in VO, Decision Support System and social network analysis approaches are integrated. In such situations social learning helps an organization in terms of relationship, team formation, partner selection etc. In this paper we focus on trust learning. Trust learning is an important activity in terms of information exchange, negotiation, collaboration and trust assessment for cooperation among virtual team members. In this paper we have proposed a reinforcement learning which enhances the trust decision making capability of interacting agents during collaboration in problem solving activity. Trust computational model with learning that we present is adapted for best alternate selection of new project in the organization. We verify our model in a multi-agent simulation where the agents in the community learn to identify trustworthy members, inconsistent behavior and conflicting behavior of agents.

Keywords: Collaborative Decision making, Trust, Multi Agent System (MAS), Bayesian Network, Reinforcement Learning.

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Authors: Jalil Boudjadar

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Dynamic Voltage and Frequency Scaling (DVFS) multicore platforms are promising execution platforms that enable high computational performance, less energy consumption and flexibility in scheduling the system processes. However, the resulting interleaving and memory interference together with per-core frequency tuning make real-time guarantees hard to be delivered. Besides, energy consumption represents a strong constraint for the deployment of such systems on energy-limited settings. Identifying the system configurations that would achieve a high performance and consume less energy while guaranteeing the system schedulability is a complex task in the design of modern embedded systems. This work studies the trade-off between energy consumption, cores utilization and memory bottleneck and their impact on the schedulability of DVFS multicore time-critical systems with a hierarchy of shared memories. We build a model-based framework using Parametrized Timed Automata of UPPAAL to analyze the mutual impact of performance, energy consumption and schedulability of DVFS multicore systems, and demonstrate the trade-off on an actual case study.

Keywords: Time-critical systems, multicore systems, schedulability analysis, performance, memory interference, energy consumption.

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Authors: Sara M. Ismael, Ali H. Al-Badi

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The rapid development and growth of technology has changed the method of obtaining information for educators and learners. Technology has created a new world of collaboration and communication among people. Incorporating new technology into the teaching process can enhance learning outcomes. Billions of individuals across the world are now connected together, and are cooperating and contributing their knowledge and intelligence. Time is no longer wasted in waiting until the teacher is ready to share information as learners can go online and get it immediatelt.

The objectives of this paper are to understand the reasons why changes in teaching and learning methods are necessary, to find ways of improving them, and to investigate the challenges that present themselves in the adoption of new ICT tools in higher education institutes.

 To achieve these objectives two primary research methods were used: questionnaires, which were distributed among students at higher educational institutes and multiple interviews with faculty members (teachers) from different colleges and universities, which were conducted to find out why teaching and learning methodology should change.

The findings show that both learners and educators agree that educational technology plays a significant role in enhancing instructors’ teaching style and students’ overall learning experience; however, time constraints, privacy issues, and not being provided with enough up-to-date technology do create some challenges.

Keywords: E-books, educational technology, educators, e-learning, learners, social media, Web 2.0, LMS.

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Authors: K. M. Ngcobo, S. D. Eyono Obono

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Science and technology has a major impact on many societal domains such as communication, medicine, food, transportation, etc. However, this dominance of modern technology can have a negative unintended impact on indigenous systems, and in particular on indigenous foods. This problem serves as a motivation to this study whose aim is to examine the perceptions of learners on the usefulness of Information and Communication Technologies (ICTs) for learning about indigenous foods. This aim will be subdivided into two types of research objectives. The design and identification of theories and models will be achieved using literature content analysis. The objective on the empirical testing of such theories and models will be achieved through the survey of Hospitality studies learners from different schools in the iLembe and Umgungundlovu Districts of the South African Kwazulu-Natal province. SPSS is used to quantitatively analyze the data collected by the questionnaire of this survey using descriptive statistics and Pearson correlations after the assessment of the validity and the reliability of the data. The main hypothesis behind this study is that there is a connection between the demographics of learners, their perceptions on the usefulness of ICTs for learning about indigenous foods, and the following personality and eLearning related theories constructs: Computer self-efficacy, Trust in ICT systems, and Conscientiousness; as suggested by existing studies on learning theories. This hypothesis was fully confirmed by the survey conducted by this study except for the demographic factors where gender and age were not found to be determinant factors of learners’ perceptions on the usefulness of ICTs for learning about indigenous foods.

Keywords: E-learning, Indigenous Foods, Information and Communication Technologies, Learning Theories, Personality.

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Authors: Ani Grubišić, Slavomir Stankov, Branko Žitko

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This paper describes a concept of stereotype student model in adaptive knowledge acquisition e-learning system. Defined knowledge stereotypes are based on student's proficiency level and on Bloom's knowledge taxonomy. The teacher module is responsible for the whole adaptivity process: the automatic generation of courseware elements, their dynamic selection and sorting, as well as their adaptive presentation using templates for statements and questions. The adaptation of courseware is realized according to student-s knowledge stereotype.

Keywords: Adaptive e-learning systems, adaptive courseware, stereotypes, Bloom's knowledge taxonomy.

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Authors: Tlili-Guiassa Yamina

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Many natural language expressions are ambiguous, and need to draw on other sources of information to be interpreted. Interpretation of the e word تعاون to be considered as a noun or a verb depends on the presence of contextual cues. To interpret words we need to be able to discriminate between different usages. This paper proposes a hybrid of based- rules and a machine learning method for tagging Arabic words. The particularity of Arabic word that may be composed of stem, plus affixes and clitics, a small number of rules dominate the performance (affixes include inflexional markers for tense, gender and number/ clitics include some prepositions, conjunctions and others). Tagging is closely related to the notion of word class used in syntax. This method is based firstly on rules (that considered the post-position, ending of a word, and patterns), and then the anomaly are corrected by adopting a memory-based learning method (MBL). The memory_based learning is an efficient method to integrate various sources of information, and handling exceptional data in natural language processing tasks. Secondly checking the exceptional cases of rules and more information is made available to the learner for treating those exceptional cases. To evaluate the proposed method a number of experiments has been run, and in order, to improve the importance of the various information in learning.

Keywords: Arabic language, Based-rules, exceptions, Memorybased learning, Tagging.

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Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

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The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: Convolutional neural networks, deep learning, foot recognition, knee rehabilitation.

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Authors: Prateep Wajeetongratana

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The purpose of this research was to study the influence of learning efficiency on local accountants’ job performance effectiveness. This paper drew upon the survey data collected from 335 local accountants survey conducted at Nakhon Ratchasima province, Thailand. The statistics utilized in this paper included percentage, mean, standard deviation, and regression analysis. The findings revealed that the majority of samples were between 31-40 years old, married, held an undergraduate degree, and had an average income between 10,000-15,000 baht. The majority of respondents had less than five years of accounting experience and worked for local administrations. The overall learning efficiency score was in the highest level while the local accountants’ job performance effectiveness score was also in the high level. The hypothesis testing’s result disclosed that learning efficiency factors which were knowledge, Skill, and Attitude had an influence on local accountants’ job the performance effectiveness.

Keywords: Accountants, Leaning Efficiency, Performance Effectiveness.

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Authors: Asma Alyahya

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This study attempts to elicit the perceptions and attitudes of EFL learners of the Preparatory Year Program at KSU towards dialogue journal writing as an EFL learning strategy. The descriptive research design used incorporated both qualitative and quantitative instruments to accomplish the objectives of the study. A learners’ attitude questionnaire and follow-up interviewswith learners from a randomly selected representative sample of the participants were employed. The participants were 55 female Saudi university students in the Preparatory Year Program at King Saud University. The analysis of the results indicated that the PYP learners had highly positive attitudes towards dialogue journal writing in their EFL classes and positive perceptions of the benefits of the use of dialogue journal writing as an EFL learning strategy. The results also revealed that dialogue journals are considered an effective EFL learning strategy since they fulfill various needs for both learners and instructors. Interestingly, the analysis of the results also revealed that Saudi university level students tend to write about personal topics in their dialogue journals more than academic ones.

Keywords: Dialogue journals, EFL, learning strategy, writing.

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Authors: M.Victoria Garcia-Camba, M.Isabel Garcia-Planas

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The use of brain stem auditory evoked potential (BAEP) is a common way to study the hearing function of people, a way to learn the functionality of a part of the brain neuronal groups that intervene in the learning process by studying the behaviour of wave VI. The latest advances in neuroscience have revealed the existence of different brain activity in the learning process that can be highlighted through the use of innocuous, low-cost and easy-access techniques such as, among others, the BAEP that can help us to detect early possible neurodevelopmental difficulties for their subsequent assessment and cure. To date and the authors best knowledge, only the latency data obtained, observing the first to V waves and mainly in the left ear, were taken into account. This work shows that it is essential to consider both ears; with these latest data, it has been possible to diagnose more precisely some cases than with the previous data had been diagnosed as “normal”despite showing signs of some alteration that motivated the new consultation to the specialist.

Keywords: Ear, neurodevelopment, auditory evoked potentials, intervals of normality, learning disabilities.

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Authors: Haiyan Wang

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Constructivism, the latest teaching and learning theory in western countries which is based on the premise that cognition (learning) is the result of "mental construction", lays emphasis on the learner's active learning. Guided by constructivism, this thesis discusses the teaching plan and its application in extensive reading course. In extensive reading classroom, emphasis should be laid on the activation of students' prior knowledge, grasping the skills of fast reading and the combination of reading and writing to check extracurricular reading. With three factors supplementing each other, students' English reading ability can be improved effectively.

Keywords: Constructivism, extensive reading, constructivist teaching.

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Authors: Kemal Izci

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Assessment forms an important part of instruction. Assessment that aims to support learning is known as formative assessment and it contributes student’s learning gain and motivation. However, teachers rarely use assessment formatively to aid their students’ learning. Thus, reviewing the factors that limit or support teachers’ practices of formative assessment will be crucial for guiding educators to support prospective teachers in using formative assessment and also eliminate limiting factors to let practicing teachers to engage in formative assessment practices during their instruction. The study, by using teacher’s change environment framework, reviews literature on formative assessment and presents a tentative model that illustrates the factors impacting teachers’ adoption of formative assessment in their teaching. The results showed that there are four main factors consisting personal, contextual, resource-related and external factors that influence teachers’ practices of formative assessment.

Keywords: Assessment practices, formative assessment, teachers, factors for use of formative assessment.

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Authors: Elizabeth Downs, Judith Repman, Kenneth Clark

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This paper describes the evolution of strategies to evaluate ePortfolios in an online Master-s of Education (M.Ed.) degree in Instructional Technology. The ePortfolios are required as a culminating activity for students in the program. By using Web 2.0 tools to develop the ePortfolios, students are able to showcase their technical skills, integrate national standards, demonstrate their professional understandings, and reflect on their individual learning. Faculty have created assessment strategies to evaluate student achievement of these skills. To further develop ePortfolios as a tool promoting authentic learning, faculty are moving toward integrating transparency as part of the evaluation process.

Keywords: e-learning evaluation, ePortfolios, transparency, Web 2.0

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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, we propose an approach leveraging the Shifted Window based Self-Attention in the Swin Transformer (Swin-GZSL) to work in the inductive GZSL 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: Yutaka Takaoka, Mika Ohta, Aki Sugano, Tsuyoshi Oda, Eiichi Maeda, Sumiyo Hanaoka, Masako Matsuura

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Along with the increased morbidity of glaucoma or diabetic retinitis pigmentosa, etc., number of people with vision loss is also increasing in Japan. It is difficult for the visually impaired to learn and acquire braille because most of them are middle-aged. In addition, number of braille teachers are not sufficient and reducing in Japan, and this situation makes more difficult for the visually impaired. Therefore, we research and develop a Web-based e-learning program for tactile braille, that cooperate with braille display and voice assistance.

Keywords: Acquired visually impaired, Braille, e-learning, Tactile braille

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Authors: Prof. Chokri SLIM

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A Novel fuzzy neural network combining with support vector learning mechanism called support-vector-based fuzzy neural networks (SVBFNN) is proposed. The SVBFNN combine the capability of minimizing the empirical risk (training error) and expected risk (testing error) of support vector learning in high dimensional data spaces and the efficient human-like reasoning of FNN.

Keywords: Neural network, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression.

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Authors: N. Chitanand

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Given the dynamic nature of the higher education landscape, induction programmes for new academics has become the norm nowadays to support academics negotiate these rough terrain. This study investigates an induction programme for new academics in a higher education institution to establish what difference it has made to participants. The findings revealed that the benefits ranged from creating safe spaces for collaboration and networking to fostering reflective practice and contributing to the scholarship of teaching and learning. The study also revealed that some of the intentions of the programme may not have been achieved, for example transformative learning. This led to questioning whether this intention is an appropriate one given the short duration of the programme and the long, drawn out process of transformation. It may be concluded that the academic induction programme in this study serves to sow the seeds for transformative learning through fostering critically reflective practice. Recommendations for further study could include long term impact of the programme on student learning and success, these being the core business of higher education. It is also recommended that in addition to an induction programme, the university invests in a mentoring programme for new staff and extend the support for academics in order to sustain critical reflection and which may contribute to transformative educational practice.

Keywords: Induction programme, reflective practice, scholarship of teaching, transformative learning.

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Authors: Douglas A. Menezes, Isabel D. Nunes, Ulrich Schiel

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Most Virtual Learning Environments do not provide support mechanisms for the integrated planning, construction and follow-up of Instructional Design supported by Learning Analytic results. The present work aims to present an authoring tool that will be responsible for constructing the structure of an Instructional Design (ID), without the data being altered during the execution of the course. The visual interface aims to present the critical situations present in this ID, serving as a support tool for the course follow-up and possible improvements, which can be made during its execution or in the planning of a new edition of this course. The model for the ID is based on High-Level Petri Nets and the visualization forms are determined by the specific kind of the data generated by an e-course, a population of students generating sequentially dependent data.

Keywords: Educational data visualization, high-level petri nets, instructional design, learning analytics.

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Authors: M. Phankokkruad, K. Woraratpanya

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This paper proposes a Web service and serviceoriented architecture (SOA) for a computer-adaptive testing (CAT) process on e-learning systems. The proposed architecture is developed to solve an interoperability problem of the CAT process by using Web service. The proposed SOA and Web service define all services needed for the interactions between systems in order to deliver items and essential data from Web service to the CAT Webbased application. These services are implemented in a XML-based architecture, platform independence and interoperability between the Web service and CAT Web-based applications.

Keywords: Web service, service-oriented architecture, computer-adaptive testing, e-learning, interoperability

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Authors: Kazunari Meguro, Daisuke Yamamoto, Shinichi Motomura, Toshihiko Sasama, Takao Kawamura, Kazunori Sugahara

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We have developed a distributed asynchronous Web based training system. In order to improve the scalability and robustness of this system, all contents and functions are realized on mobile agents. These agents are distributed to computers, and they can use a Peer to Peer network that modified Content-Addressable Network. In the proposed system, only text data can be included in a exercise. To make our proposed system more useful, the mechanism that it not only adapts to multimedia data but also it doesn-t influence the user-s learning even if the size of exercise becomes large is necessary.

Keywords: e-Learning, multimedia, Mobile Agent.

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Authors: Yahya H. Zweiri

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The back-propagation algorithm calculates the weight changes of an artificial neural network, and a two-term algorithm with a dynamically optimal learning rate and a momentum factor is commonly used. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third term increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and optimization approaches for evaluating the learning parameters are required to facilitate the application of the three terms BP algorithm. This paper considers the optimization of the new back-propagation algorithm by using derivative information. A family of approaches exploiting the derivatives with respect to the learning rate, momentum factor and proportional factor is presented. These autonomously compute the derivatives in the weight space, by using information gathered from the forward and backward procedures. The three-term BP algorithm and the optimization approaches are evaluated using the benchmark XOR problem.

Keywords: Neural Networks, Backpropagation, Optimization.

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Authors: Mohammadhosein Hasanbeig, Lacra Pavel

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In this paper, we study a distributed control algorithm for the problem of unknown area coverage by a network of robots. The coverage objective is to locate a set of targets in the area and to minimize the robots’ energy consumption. The robots have no prior knowledge about the location and also about the number of the targets in the area. One efficient approach that can be used to relax the robots’ lack of knowledge is to incorporate an auxiliary learning algorithm into the control scheme. A learning algorithm actually allows the robots to explore and study the unknown environment and to eventually overcome their lack of knowledge. The control algorithm itself is modeled based on game theory where the network of the robots use their collective information to play a non-cooperative potential game. The algorithm is tested via simulations to verify its performance and adaptability.

Keywords: Distributed control, game theory, multi-agent learning, reinforcement learning.

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Authors: R. Maier, S. Thalmann, A. Sandow

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Organizations face challenges supporting knowledge workers due to their particular requirements for an environment supportive of their self-guided learning activities which are important to increase their productivity and to develop creative solutions to non-routine problems. Face-to-face knowledge sharing remains crucial in spite of a large number of knowledge management instruments that aim at supporting a more impersonal transfer of knowledge. This paper first describes the main criteria for a conceptual and technical solution targeted at flexible management of office space that aims at assigning those knowledge workers to the same room that are most likely to thrive when being brought together thus enhancing their knowledge work productivity. The paper reflects on lessons learned from the implementation and operation of such a solution in a project-focused organization and derives several implications for future extensions that target to foster problem solving, informal learning and personal development.

Keywords: informal learning, knowledge work, officemanagement.

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Authors: Stuart Palmer, Dale Holt

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This paper presents a large scale, quantitative investigation of the impact of discipline differences on the student experience of using an online learning environment (OLE). Based on a representative sample of 2526 respondents, a number of significant differences in the mean rating by broad discipline area of the importance of, and satisfaction with, a range of elements of an OLE were found. Broadly speaking, the Arts and Science and Technology discipline areas reported the lowest importance and satisfaction ratings for the OLE, while the Health and Behavioural Sciences area was the most satisfied with the OLE. A number of specific, systematic discipline differences are reported and discussed. Compared to the observed significant differences in mean importance ratings, there were fewer significant differences in mean satisfaction ratings, and those that were observed were less systematic than for importance ratings.

Keywords: Discipline difference, learning management system, online learning environment, student evaluation.

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