Search results for: optimum learning outcomes

Authors: Rujuta Matapurkar, Shivganesh Bhargava

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This paper discusses work-family enrichment and its relationship with certain antecedents and outcomes while considering effect of mindfulness and organizational pride as moderators. The work-family enrichment has been the topic of interest for researchers as well as practitioners for decades now. It focusses on the positive side of work family interaction rather that the scarcity or balance principle. Research shows that work family enrichment is linked to multiple work place outcomes like job satisfaction, organization citizenship behavior and turnover intention. Enrichment is also linked to life outcomes like life satisfaction, wellbeing. Thus not only the individuals but the organizations too want to engage in the activities resulting in the positive spillover between work and non-work domains. One of the recent focus areas in organization behavior literature has been Mindfulness. Mindfulness is defined as a trait or state in which the mind focuses on the present. It is the conscious attention and awareness of the present thought. The research in the area of mindfulness at work suggests that the same is related to work family balance and job satisfaction. This paper discusses the possibility of mindfulness having effect on the relationship between antecedents of enrichment and enrichment. On the outcome side job embeddedness and job ambivalence are the newest additions to the retention literature. Job ambivalence talks about having strong positive as well as negative feelings about the job. Job ambivalence is the work outcome which is linked to turnover intention. This paper talks about the relationship between enrichment and job ambivalence. Another measure for work place outcomes which is discussed in recent research is job embeddedness. This term talks about the advantages of continuing with the job rather than quitting it. It is described as like a net or a web in which an individual can become stuck and is focused on why people stay rather than on how they leave. The research has have found that establishing or increasing job embeddedness is likely to increase retention, attendance, citizenship and job performance. This paper studies the relationship between enrichment and embeddedness. Lastly this paper studies whether organizational pride has an an effect on the relationship between enrichment and its outcomes. This paper concludes with the direction for future research.

Keywords: work-family enrichment, mindfulness, job ambivalence, job embeddedness, organizational pride

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Authors: Yolina A. Petrova, Georgi I. Petkov

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The paper presents a symbolic model for category learning and categorization (called RoleMap). Unlike the other models which implement learning in a separate working mode, role-governed category learning and categorization emerge in RoleMap while it does its usual reasoning. The model is based on several basic mechanisms known as reflecting the sub-processes of analogy-making. It steps on the assumption that in their everyday life people constantly compare what they experience and what they know. Various commonalities between the incoming information (current experience) and the stored one (long-term memory) emerge from those comparisons. Some of those commonalities are considered to be highly important, and they are transformed into concepts for further use. This process denotes the category learning. When there is missing knowledge in the incoming information (i.e. the perceived object is still not recognized), the model makes anticipations about what is missing, based on the similar episodes from its long-term memory. Various such anticipations may emerge for different reasons. However, with time only one of them wins and is transformed into a category member. This process denotes the act of categorization.

Keywords: analogy-making, categorization, category learning, cognitive modeling, role-governed categories

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Authors: Pauline Calleja, Brooke Alexander

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In 2010 the College of Emergency Nursing Australasia (CENA) undertook sole administration of the Trauma Nursing Program (TNP) across Australia. The original TNP was developed from recommendations by the Review of Trauma and Emergency Services-Victoria. While participant and faculty feedback about the program was positive, issues were identified that were common for industry training programs in Australia. These issues included didactic approaches, with many lectures and little interaction/activity for participants. Participants were not necessarily encouraged to undertake deep learning due to the teaching and learning principles underpinning the course, and thus participants described having to learn by rote, and only gain a surface understanding of principles that were not always applied to their working context. In Australia, a trauma or emergency nurse may work in variable contexts that impact on practice, especially where resources influence scope and capacity of hospitals to provide trauma care. In 2011, a program review was undertaken resulting in major changes to the curriculum, teaching, learning and assessment approaches. The aim was to improve learning including a greater emphasis on pre-program preparation for participants, the learning environment and clinically applicable contextualized outcomes participants experienced. Previously if participants wished to undertake assessment, they were given a take home examination. The assessment had poor uptake and return, and provided no rigor since assessment was not invigilated. A new assessment structure was enacted with an invigilated examination during course hours. These changes were implemented in early 2012 with great improvement in both faculty and participant satisfaction. This presentation reports on a comparison of participant evaluations collected from courses post implementation in 2012 and in 2015 to evaluate if positive changes were sustained. Methods: Descriptive statistics were applied in analyzing evaluations. Since all questions had more than 20% of cells with a count of <5, Fisher’s Exact Test was used to identify significance (p = <0.05) between groups. Results: A total of fourteen group evaluations were included in this analysis, seven CENA TNP groups from 2012 and seven from 2015 (randomly chosen). A total of 173 participant evaluations were collated (n = 81 from 2012 and 92 from 2015). All course evaluations were anonymous, and nine of the original 14 questions were applicable for this evaluation. All questions were rated by participants on a five-point Likert scale. While all items showed improvement from 2012 to 2015, significant improvement was noted in two items. These were in regard to the content being delivered in a way that met participant learning needs and satisfaction with the length and pace of the program. Evaluation of written comments supports these results. Discussion: The aim of redeveloping the CENA TNP was to improve learning and satisfaction for participants. These results demonstrate that initial improvements in 2012 were able to be maintained and in two essential areas significantly improved. Changes that increased participant engagement, support and contextualization of course materials were essential for CENA TNP evolution.

Keywords: emergency nursing education, industry training programs, teaching and learning, trauma education

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Authors: Susan Diamond

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Machine Learning workloads have traditionally been run in high-performance computing (HPC) environments, where users log in to dedicated machines and utilize the attached GPUs to run training jobs on huge datasets. Training of large neural network models is very resource intensive, and even after exploiting parallelism and accelerators such as GPUs, a single training job can still take days. Consequently, the cost of hardware is a barrier to entry. Even when upfront cost is not a concern, the lead time to set up such an HPC environment takes months from acquiring hardware to set up the hardware with the right set of firmware, software installed and configured. Furthermore, scalability is hard to achieve in a rigid traditional lab environment. Therefore, it is slow to react to the dynamic change in the artificial intelligent industry. Watson Deep Learning as a service, a cloud-based deep learning platform that mitigates the long lead time and high upfront investment in hardware. It enables robust and scalable sharing of resources among the teams in an organization. It is designed for on-demand cloud environments. Providing a similar user experience in a multi-tenant cloud environment comes with its own unique challenges regarding fault tolerance, performance, and security. Watson Deep Learning as a service tackles these challenges and present a deep learning stack for the cloud environments in a secure, scalable and fault-tolerant manner. It supports a wide range of deep-learning frameworks such as Tensorflow, PyTorch, Caffe, Torch, Theano, and MXNet etc. These frameworks reduce the effort and skillset required to design, train, and use deep learning models. Deep Learning as a service is used at IBM by AI researchers in areas including machine translation, computer vision, and healthcare. 

Keywords: deep learning, machine learning, cognitive computing, model training

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Authors: Xun Li, Haojie Wang

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Federated learning is a kind of distributed training and centralized training mode, which is of great value in the protection of user privacy. In order to solve the problem that the model is vulnerable to backdoor attacks in federated learning, a backdoor attack detection method based on a weighted k-medoids algorithm is proposed. First of all, this paper collates the update parameters of the client to construct a vector group, then uses the principal components analysis (PCA) algorithm to extract the corresponding feature information from the vector group, and finally uses the improved k-medoids clustering algorithm to identify the normal and backdoor update parameters. In this paper, the backdoor is implanted in the federation learning model through the model replacement attack method in the simulation experiment, and the update parameters from the attacker are effectively detected and removed by the defense method proposed in this paper.

Keywords: federated learning, backdoor attack, PCA, k-medoids, backdoor defense

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Authors: Anum Ali, Kashaf ad Dooja, Asif Saleem

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The future smart cities trend will be towards Internet of Things (IoT); IoT creates dynamic connections in a ubiquitous manner. Smart cities offer ease and flexibility for daily life matters. By using small devices that are connected to cloud servers based on IoT, network traffic between these devices is growing exponentially, whose security is a concerned issue, since ratio of cyber attack may make the network traffic vulnerable. This paper discusses the latest machine learning approaches in related work further to tackle the increasing rate of cyber attacks, machine learning algorithm is applied to IoT-based network traffic data. The proposed algorithm train itself on data and identify different sections of devices interaction by using supervised learning which is considered as a classifier related to a specific IoT device class. The simulation results clearly identify the attacks and produce fewer false detections.

Keywords: IoT, traffic security, deep learning, classification

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Authors: Anastassis Kozanitis, Diane Leduc, Alain Stockless

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This paper presents preliminary results from a two-year funded research program looking to analyze and understand the relationship between high cognitive engagement, higher order cognitive processes employed in situations of complex learning tasks, and the use of active learning pedagogies in engineering undergraduate programs. A mixed method approach was used to gauge student engagement and their cognitive processes when accomplishing complex tasks. Quantitative data collected from the self-report cognitive engagement scale shows that deep learning approach is positively correlated with high levels of complex learning tasks and the level of student engagement, in the context of classroom active learning pedagogies. Qualitative analyses of in depth face-to-face interviews reveal insights into the mechanisms influencing students’ cognitive processes when confronted with open-ended problem resolution. Findings also support evidence that students will adjust their level of cognitive engagement according to the specific didactic environment.

Keywords: cognitive engagement, deep and shallow strategies, engineering programs, higher order cognitive processes

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Authors: Soltani Amir, Hu Jiaxin

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Determination of optimal parameters of a passive control system device is the primary objective of this study. Expanding upon the use of control devices in wind and earthquake hazard reduction has led to development of various control systems. The advantage of non-linearity characteristics in a passive control device and the optimal control method using LQR algorithm are explained in this study. Finally, this paper introduces a simple approach to determine optimum parameters of a nonlinear viscous damper for vibration control of structures. A MATLAB program is used to produce the dynamic motion of the structure considering the stiffness matrix of the SDOF frame and the non-linear damping effect. This study concluded that the proposed system (variable damping system) has better performance in system response control than a linear damping system. Also, according to the energy dissipation graph, the total energy loss is greater in non-linear damping system than other systems.

Keywords: passive control system, damping devices, viscous dampers, control algorithm

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Authors: Luis H. Gonzalez-Guerra, Armandina J. Leal-Flores

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Students attending classrooms nowadays are habituated to use digital devices all the time and for multiple things. They have been familiar with digital technology throughout their lives so they have developed skills that should be naturally adopted as part of their study strategies. New learning styles require taking in consideration the use of models that support and promote student motivation for learning and development of their creative thinking skills. To achieve student learning in programming courses, different strategies are used. One of them is a collaboration between students, which is a tool which faculty can take advantage of when teaching these kinds of courses. Moreover, cooperation is an essential skill that society should reinforce in order to promote a healthy social environment and cohabitation. Nevertheless, students will still require support and advice to get a complete and correct programming solution to successfully address and solve the problems given throughout the course. This paper present a model where collaboration between students is associated with an automatic tutoring platform providing an excellent approach for the individual learning in collaborative activities in programming courses, and also motivates students to increase their knowledge regarding the topics covered in the classroom.

Keywords: automatic tutoring, collaboration learning, creative thinking, motivation

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Authors: Daniel Ecler

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The aim of this paper was to find out how Chinese university students perceive distance learning compared to full-time teaching, to reveal its advantages and disadvantages, and to try to find what elements could be implemented in regular full-time teaching in order to make it more effective. Recent events have shown that online teaching has a significant role to play in the field of education and needs to be given increased attention and scrutiny. For this purpose, a research survey was conducted using semi-structured questionnaires, which aimed to determine the attitudes of Chinese university students to the phenomenon of distance learning. The results of this survey revealed that most students prefer distance learning to full-time teaching, mainly because it gives them more freedom to participate in teaching, regardless of the environment in which they are currently located. In conclusion, it is necessary to mention that the possibility to participate virtually in teaching from anywhere is a huge advantage that could become part of regular teaching in the future. However, further research into this issue will be necessary.

Keywords: distance learning, full-time teaching, Chinese college students, cultural background

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Authors: Matthias Grunke

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In this qualitative study, we interviewed a young man with learning disabilities who played professional baseball for two years. Individuals with severe academic challenges constitute one of the most vulnerable groups of our society. Science has to find ways on how to arm them against life’s challenges and help them to cope with the many risk factors that they are usually confronted with. Team sports like baseball seem to be a suitable means for that purpose. In the interview, our participant talked about his life as a student with severe learning difficulties and related how his career in baseball made his academic challenges appear much less significant. He gave some meaningful insights into what helped him to build a happy and fulfilling life for himself, not only in spite of his challenges but also because of what he's learning disabilities taught him. Support from significant others, a sense of purpose, his fighting spirit ignited by sports, and the success that he experienced on the baseball field were among the most relevant factors. Overall, this study highlights the importance of finding an outlet for young people with learning disabilities where their academic difficulties retreat into the background and their talents are validated.

Keywords: baseball, inclusion, learning disabilities, resilience

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Authors: Akaash Tawade, Manan Khattar, Lucas Spangher, Costas J. Spanos

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Many grids are increasing the share of renewable energy in their generation mix, which is causing the energy generation to become less controllable. Buildings, which consume nearly 33% of all energy, are a key target for demand response: i.e., mechanisms for demand to meet supply. Understanding the behavior of office workers is a start towards developing demand response for one sector of building technology. The literature notes that dynamic computational modeling can be predictive of individual action, especially given that occupant behavior is traditionally abstracted from demand forecasting. Recent work founded on Social Cognitive Theory (SCT) has provided a promising conceptual basis for modeling behavior, personal states, and environment using control theoretic principles. Here, an adapted linear dynamical system of latent states and exogenous inputs is proposed to simulate energy demand amongst office workers engaged in a social energy shifting game. The energy shifting competition is implemented in an office in Singapore that is connected to a minigrid of buildings with a consistent 'price signal.' This signal is translated into a 'points signal' by a reinforcement learning (RL) algorithm to influence participant energy use. The dynamic model functions at the intersection of the points signals, baseline energy consumption trends, and SCT behavioral inputs to simulate future outcomes. This study endeavors to analyze how the dynamic model trains an RL agent and, subsequently, the degree of accuracy to which load deferability can be simulated. The results offer a generalizable behavioral model for energy competitions that provides the framework for further research on transfer learning for RL, and more broadly— transactive control.

Keywords: energy demand forecasting, social cognitive behavioral modeling, social game, transfer learning

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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: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on HSI dataset Indian Pines. The results confirm the capability of the proposed method.

Keywords: continual learning, data reconstruction, remote sensing, hyperspectral image segmentation

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Authors: Adedolapo Taiwo Adeyemi, Success Ayodeji Fasanmi

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The emergence of the coronavirus pandemic and the rate of the spread affected a lot of activities across the world. This led to the introduction of online learning modes in several countries after institutions were shut down. Unfortunately, most public universities in Nigeria could not switch to the online mode because they were not prepared for it, as they do not have the technological capacity to support a full online learning mode. This study examines the readiness of university and the attitude of undergraduates towards online learning mode in Obafemi Awolowo University (OAU), Ile Ife. It investigated the skills and competencies of students for online learning as well as the university’s readiness towards online learning mode; the effort was made to identify challenges of online teaching and learning in the study area, and suggested solutions were advanced. OAU was selected because it is adjudged to be the leading Information and Communication Technology (ICT) driven institution in Nigeria. The descriptive survey research design was used for the study. A total of 256 academic staff and 1503 undergraduates were selected across six faculties out of the thirteen faculties in the University. Two set of questionnaires were used to get responses from the selected respondents. The result showed that students have the skills and competence to operate e-learning facilities but are faced with challenges such as high data cost, erratic power supply, and lack of gadgets, among others. The study found out that the university was not prepared for online learning mode as it lacks basic technological facilities to support it. The study equally showed that while lecturers possess certain skills in using some e-learning applications, they were limited by the unavailability of online support gadgets, poor internet connectivity, and unstable power supply. Furthermore, the assessment of student attitude towards online learning mode shows that the students found the online learning mode very challenging as they had to bear the huge cost of data. Lecturers also faced the same challenge as they had to pay a lot to buy data, and the networks were sometimes unstable. The study recommended that adequate funding needs to be provided to public universities by the government while the management of institutions must build technological capacities to support online learning mode in the hybrid form and on a full basis in case of future emergencies.

Keywords: universities, online learning, undergraduates, attitude

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Authors: Fiona Jensen, Barb Goodwin, Nancy Kleiman, Rhonda Usunier

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Background: Clinical simulation is now a common component in many health profession curricula in preparation for clinical practice. In the Rady Faculty of Health Sciences (RFHS) college leads in simulation and interprofessional (IP) education, planned an eight hour simulated hospital day shift, where seventy students from six health professions across two campuses, learned with each other in a safe, realistic environment. Learning about interprofessional collaboration, an expected competency for many health professions upon graduation, was a primary focus of the simulation event. Method: Faculty representatives from the Colleges of Nursing, Medicine, Pharmacy and Rehabilitation Sciences (Physical Therapy, Occupation Therapy, Respiratory Therapy) and Pharmacy worked together to plan the IP event in a simulation facility in the College of Nursing. Each college provided a faculty mentor to guide the same profession students. Students were placed in interprofessional teams consisting of a nurse, physician, pharmacist, and then sharing respiratory, occupational, and physical therapists across the team depending on the needs of the patients. Eight patient scenarios were role played by health profession students, who had been provided with their patient’s story shortly before the event. Each team was guided by a facilitator. Results and Outcomes: On the morning of the event, all students gathered in a large group to meet mentors and facilitators and have a brief overview of the six competencies for effective collaboration and the session objectives. The students assuming their same profession roles were provided with their patient’s chart at the beginning of the shift, met with their team, and then completed professional specific assessments. Shortly into the shift, IP team rounds began, facilitated by the team facilitator. During the shift, each patient role-played a spontaneous health incident, which required collaboration between the IP team members for assessment and management. The afternoon concluded with team rounds, a collaborative management plan, and a facilitated de-brief. Conclusions: During the de-brief sessions, students responded to set questions related to the session learning objectives and expressed many positive learning moments. We believe that we have a sustainable simulation IP collaborative learning opportunity, which can be embedded into curricula, and has the capacity to grow to include more health profession faculties and students. Opportunities are being explored in the RFHS at the administrative level, to offer this event more frequently in the academic year to reach more students. In addition, a formally structured event evaluation tool would provide important feedback and inform the qualitative feedback to event organizers and the colleges about the significance of the simulation event to student learning.

Keywords: simulation, collaboration, teams, interprofessional

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Authors: Rodrigo Arnaldo Scarpel

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Trend following is a widely accepted investment strategy that adopts a rule-based trading mechanism that rather than striving to predict market direction or on information gathering to decide when to buy and when to sell a stock. Thus, in trend following one must respond to market’s movements that has recently happen and what is currently happening, rather than on what will happen. Optimally, in trend following strategy, is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. For applying the trend following strategy one needs to find the trend and identify trade signals. In order to avoid false signals, i.e., identify fluctuations of short, mid and long terms and to separate noise from real changes in the trend, most academic works rely on moving averages and other technical analysis indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI) to uncover intelligible stock trading rules following trend following strategy philosophy. Recently, some works has applied machine learning techniques for trade rules discovery. In those works, the process of rule construction is based on evolutionary learning which aims to adapt the rules to the current environment and searches for the global optimum rules in the search space. In this work, instead of focusing on the usage of machine learning techniques for creating trading rules, a time series trend classification employing a semi-supervised approach was used to early identify both the beginning and the end of upward and downward trends. Such classification model can be employed to identify trade signals and the decision-making procedure is that if an up-trend (down-trend) is identified, a buy (sell) signal is generated. Semi-supervised learning is used for model training when only part of the data is labeled and Semi-supervised classification aims to train a classifier from both the labeled and unlabeled data, such that it is better than the supervised classifier trained only on the labeled data. For illustrating the proposed approach, it was employed daily trade information, including the open, high, low and closing values and volume from January 1, 2000 to December 31, 2022, of the São Paulo Exchange Composite index (IBOVESPA). Through this time period it was visually identified consistent changes in price, upwards or downwards, for assigning labels and leaving the rest of the days (when there is not a consistent change in price) unlabeled. For training the classification model, a pseudo-label semi-supervised learning strategy was used employing different technical analysis indicators. In this learning strategy, the core is to use unlabeled data to generate a pseudo-label for supervised training. For evaluating the achieved results, it was considered the annualized return and excess return, the Sortino and the Sharpe indicators. Through the evaluated time period, the obtained results were very consistent and can be considered promising for generating the intended trading signals.

Keywords: evolutionary learning, semi-supervised classification, time series data, trading signals generation

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Authors: Dejan Tanikić, Miodrag Manić, Jelena Đoković, Saša Kalinović

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This paper deals with the determination of the optimum machining parameters, according to the measured and modelled data of the cutting temperature and surface roughness, during the turning of the AISI 4140 steel. The high cutting temperatures are unwanted occurences in the metal cutting process. They impact negatively on the quality of the machined part. The machining experiments were performed using different cutting regimes (cutting speed, feed rate and depth of cut), with different values of the workpiece hardness, which causes different values of the measured cutting temperature as well as the measured surface roughness. The temperature and surface roughness data were modelled after that using Response Surface Methodology (RSM). The obtained RSM models are used in the process of optimization of the cutting regimes using the Genetic Algorithms (GA) tool, which enables the metal cutting process in the optimum conditions.

Keywords: genetic algorithms, machining parameters, response surface methodology, turning process

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Authors: Atara Shriki, Ilana Lavy

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Teaching for the first time an online course dealing with the history of mathematics, we were struggling with questions related to the design of a proper learning environment (LE). Thirteen high school mathematics teachers, M.Ed. students, attended the course. The teachers were engaged in independent reading of mathematical texts, a task that is recognized as complex due to the unique characteristics of such texts. In order to support the learning processes and develop skills that are essential for succeeding in learning online (e.g. self-regulated learning skills, meta-cognitive skills, reflective ability, and self-assessment skills), the LE comprised of three components aimed at “scaffolding” the learning: (1) An online "self-feedback" questionnaires that included drill-and-practice questions. Subsequent to responding the questions the online system provided a grade and the teachers were entitled to correct their answers; (2) Open-ended questions aimed at stimulating critical thinking about the mathematical contents; (3) Reflective questionnaires designed to assist the teachers in steering their learning. Using a mixed-method methodology, an inquiry study examined the learning processes, the learners' difficulties in reading the mathematical texts and on the unique contribution of each component of the LE to the ability of teachers to comprehend the mathematical contents, and support the development of their learning skills. The results indicate that the teachers found the online feedback as most helpful in developing self-regulated learning skills and ability to reflect on deficiencies in knowledge. Lacking previous experience in expressing opinion on mathematical ideas, the teachers had troubles in responding open-ended questions; however, they perceived this assignment as nurturing cognitive and meta-cognitive skills. The teachers also attested that the reflective questionnaires were useful for steering the learning. Although in general the teachers found the LE as supportive, most of them indicated the need to strengthen instructor-learners and learners-learners interactions. They suggested to generate an online forum to enable them receive direct feedback from the instructor, share ideas with other learners, and consult with them about solutions. Apparently, within online LE, supporting learning merely with respect to cognitive aspects is not sufficient. Leaners also need an emotional support and sense a social presence.

Keywords: cognitive and meta-cognitive skills, independent reading of mathematical texts, online learning environment, self-regulated learning skills

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Authors: Qing Wu, Fei-Yan Li, Heng-Chang Zhang

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Multitask learning support vector machines (SVMs) have recently attracted increasing research attention. Given several related tasks, the single-task learning methods trains each task separately and ignore the inner cross-relationship among tasks. However, multitask learning can capture the correlation information among tasks and achieve better performance by training all tasks simultaneously. In addition, the asymmetric squared loss function can better improve the generalization ability of the models on the most asymmetric distributed data. In this paper, we first make two assumptions on the relatedness among tasks and propose two multitask learning proximal support vector machine algorithms, named MTL-a-PSVM and EMTL-a-PSVM, respectively. MTL-a-PSVM seeks a trade-off between the maximum expectile distance for each task model and the closeness of each task model to the general model. As an extension of the MTL-a-PSVM, EMTL-a-PSVM can select appropriate kernel functions for shared information and private information. Besides, two corresponding special cases named MTL-PSVM and EMTLPSVM are proposed by analyzing the asymmetric squared loss function, which can be easily implemented by solving linear systems. Experimental analysis of three classification datasets demonstrates the effectiveness and superiority of our proposed multitask learning algorithms.

Keywords: multitask learning, asymmetric squared loss, EMTL-a-PSVM, classification

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Authors: Hong-Yi Kuo

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Leadership is a key factor of improving workplace safety, and there have been abundant of studies which support the positive effects of appropriate leadership on employee safety performance in the western academic. However, little safety research focus on the Chinese leadership style like paternalistic leadership. To fill this gap, the resent study aims to examine the relationship between authoritarian leadership (one of the ternary mode in paternalistic leadership) and safety outcomes. This study makes hypothesis on different levels. First, on the group level, as an authoritarian leader regards safety value as the most important tasks, there would be positive effect on group safety outcomes through strengthening safety group norms by the emphasis on etiquette. Second, on the cross level, when a leader with authoritarian style has high priority on safety, employees may more obey the safety rules because of fear due to emphasis on absolute authority over the leader. Therefore, employees may show more safety performance and then increase individual safety outcomes. Survey data would be collected from 50 manufacturing groups (each group with more than 5 members and a leader) and a hierarchical linear modeling analysis would be conducted to analyze the hypothesis. Above the predictive result, the study expects to be a cornerstone of safety leadership research in the Chinese academic and practice.

Keywords: safety leadership, authoritarian leadership, group norms, safety behavior, supervisor safety priority

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Authors: Ram A. Giri, Amna Bedri, Abdou Niane

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Exclusion in education on the basis of language in multilingual contexts operates at multiple levels. Learners of diverse ethnolinguistic backgrounds are often expected to learn through English and are pushed further down the learning ladder if they also have to access education through Information and Communication Technology (ICT). The paper explores marginalized children’s lived experiences in accessing technology and English in four low-income countries in Africa and Asia. Based on the findings of the first phase of a multinational qualitative research study, we report on the factors or barriers that affect children’s access, opportunities and motivation for learning through technology and English. ICT and English - the language of ICT and education - can enhance learning and can even be essential. However, these two important keys to education can also function as barriers to accessing quality education, and therefore as zones of exclusion. This paper looks into how marginalized children (aged 13-15) engage in learning through ICT and English and to what extent the restrictive access and opportunities contribute to the widening of the already existing gap in education. By applying the conceptual frameworks of “access and accessibility of learning” and “zones of exclusion,” the paper elucidates how the barriers prevent children’s effective engagement with learning and addresses such questions as to how marginalized children access technology and English for learning; whether the children value English, and what their motivation and opportunity to learn it are. In addition, the paper will point out policy and pedagogic implications.

Keywords: exclusion, inclusion, inclusive education, marginalization

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Authors: Yuqing Sun

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Chinese has been regarded as one of the most difficult languages in learning due to its complex spelling structure, difficult pronunciation, as well as its varying forms. Since vocabulary acquisition is the basic process to acquire a language, to express yourself, to compose a sentence, and to conduct a communication, so learning the vocabulary is of great importance. However, the vocabulary contains pronunciation, spelling, recognition and application which may seem as a huge work. This may pose a question for the language teachers (language teachers in China who teach Chinese to the foreign students): How to teach them in an effective way? Traditionally, teachers have no choice but teach it all by themselves, then with the development of technology, they can use computer as a tool to help them (Computer Assisted Language Learning or CALL). Now, they move into the Mobile Assisted Language Learning (MALL) method to guide their teaching, upon which the appraisal is convincing. It diversifies the learning material and the way of output, which can activate learners’ curiosity and accelerate their understanding. This paper will focus on actual case studies occurring in the universities in China of teaching the foreign students to learn Chinese, and the analysis of the utilization of WeChat channel as an example of MALL model to explore the active role of MALL to enhance the effectiveness of Chinese vocabulary acquisition.

Keywords: Chinese, vocabulary acquisition, MALL, case

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Authors: Bryan Crompton, Daniel Giger, Tanay Mehta, Apurva Mody

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The task of classifying radio frequency (RF) signals has seen recent success in employing deep neural network models. In this work, we present a combined signal processing and machine learning approach to signal classification for cochannel anomalous signals. The power spectral density and cyclostationary signal processing features of a captured signal are computed and fed into a neural net to produce a classification decision. Our combined signal preprocessing and machine learning approach allows for simpler neural networks with fast training times and small computational resource requirements for inference with longer preprocessing time.

Keywords: signal processing, machine learning, cyclostationary signal processing, signal classification

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Authors: S. Jaikeaw, S. Chavadej

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The objective of this study was to determine the effects of COD loading rate on hydrogen and methane production and micronutrient transportation using a two-stage upflow anaerobic sludge blanket (UASB) system under mesophilic temperature (37°C) with a constant recycle ratio of 1:1 (final effluent flow rate: feed flow rate). The first (hydrogen) UASB unit having 4 L liquid holding volume was controlled at pH 5.5 but the second (methane) UASB unit having 24 L liquid holding volume had no pH control. The two-stage UASB system operated at different COD loading rates from 8 to 20 kg/m³d based on total UASB working volume. The results showed that, at the optimum COD loading rate of 13 kg/m³d, the produced gas from the hydrogen UASB unit contained 1.5% H₂, 16.5% CH₄, and 82% CO₂ with H₂S of 252 ppm and also provided a hydrogen yield of 1.66 mL/g COD removed (or 0.56 mL/g COD applied) and a specific hydrogen production rate of 156.85 ml H₂/LRd (or 5.12 ml H₂/g MLVSS d). Under the optimum COD loading rate, the produced gas from the methane UASB unit mainly contained methane and carbon dioxide without hydrogen of 74 and 26%, respectively with hydrogen sulfide of 287 ppm and the system also provided a maximum methane yield of 407.00 mL/g COD removed (or 263.23 mL/g COD applied) and a specific methane production rate of 2081.44 ml CH₄/LRd (or 99.75 ml CH₄/g MLVSS d). Under the optimum COD loading rate, all micronutrients markedly dropped by the sulfide precipitation reactions. The reduction of micronutrients mostly appeared in the methane UASB unit. Under the studied conditions, both Co and Ni were found to be greatly precipitated out, causing the deficiency to microbial activity. It is hypothesized that an addition of both Co and Ni can improve the methanogenic activity.

Keywords: hydrogen and methane production, ethanol wastewater, a two-stage upflow anaerobic blanket (UASB) system, mesophillic temperature, microbial concentration (MLVSS), micronutrients

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Authors: Nikolina Ribarić

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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, motivation, Testmoz

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Authors: Laura M. Schwarz

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WebQuests are an internet-based interactive teaching/learning tool and utilize a scaffolded methodology. WebQuests employ critical thinking, afford inquiry-based constructivist learning, and readily employ Bloom’s Taxonomy. WebQuests have generally been used as instructional technology tools in primary and secondary education and have more recently grown in popularity in higher education. The study of the efficacy of WebQuests as an instructional approach to learning, however, has been limited, particularly in the nursing education arena. The purpose of this mixed-methods study was to determine nursing students’ perceptions of the effectiveness of the Nursing WebQuest as a teaching/learning strategy for holistic nursing-related content. Quantitative findings (N=42) suggested that learners were active participants, used reflection, thought of new ideas, used analysis skills, discovered something new, and assessed the worth of something while taking part in the WebQuests. Qualitative findings indicated that participants found WebQuest positives as easy to understand and navigate; clear and organized; interactive; good alternative learning format, and used a variety of quality resources. Participants saw drawbacks as requiring additional time and work; and occasional failed link or link causing them to lose their location in the WebQuest. Recommendations include using larger sample size and more diverse populations from various programs and universities. In conclusion, WebQuests were found to be an effective teaching/learning tool as positively assessed by study participants.

Keywords: holistic nursing, nursing education, teaching/learning strategy, WebQuests

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Authors: Fabio Fabris, Alex A. Freitas

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Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation.

Keywords: algorithm recommendation, meta-learning, bioinformatics, hierarchical classification

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Authors: Beazidou Eleftheria

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Roma children face discrimination in schools where they are the minority. On the other hand, teachers do not identify the specific needs of Roma students for educational and social inclusion and generally use a very restricted repertoire of insufficient strategies for helping them. Modern classrooms can and should look different. Therefore, engaging in transformational learning with young children is a deliberate choice. Transformation implies a different way of thinking and acting. This requires new knowledge that incorporates multiple perspectives and actions in order to generate experiences for further learning. In this way, we build knowledge based on empirical examples, and we share what works efficiently. The present research aims at assisting the participating teachers to improve their teaching inclusive practice, thus ultimately benefiting their students. To increase the impact of transformative efforts with a ‘new’ teaching approach, we implemented a classroom-based action research program for over six months in five kindergarten classrooms with Roma and non-Roma students. More specifically, we explore a) information about participants’ experience of the program and b) if the program is successful in helping participants to change their teaching practice. Action research is, by definition, a form of inquiry that is intended to have both action and research outcomes. The action research process that we followed included five phases: 1. Defining the problem: As teachers said, the Roma students are often the most excluded group in schools (Low social interaction and participation in classroom activities) 2. Developing a plan to address the problem: We decided to address the problem by improving/transforming the inclusive practices that teachers implemented in their classrooms. 3. Acting: implementing the plan: We incorporated new activities for all students with the goals: a) All students being passionate about their learning, b) Teachers must investigate issues in the educational context that are personal and meaningful to children's growth, c) Establishment of a new module for values and skills for all students, d) Raising awareness in culture of Roma, e) Teaching students to reflect. 4. Observing: We explore the potential for transformation in the action research program that involves observations of students’ participation in classroom activities and peer interaction. – thus, generated evidence from data. 5. Reflecting and acting: After analyzing and evaluating the outcomes from data and considering the obstacles during the program’s implementation, we established new goals for the next steps of the program. These are centered in: a) the literacy skills of Roma students and b) the transformation of teacher’s perceptions and believes, which have a powerful impact on their willingness to adopt new teaching strategies. The final evaluation of the program showed a significant achievement of the transformative goals, which were related to the active participation of the Roma students in classroom activities and peer interaction, while the activities which were related to literacy skills did not have the expected results. In conclusion, children were equipped with relevant knowledge and skills to raise their potential and contribute to wider societal development as well as teachers improved their teaching inclusive practice.

Keywords: action research, inclusive practices, kindergarten, transformation

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Authors: Vignesh Ratnaraj, Jaewon Chang

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Background: Frailty is associated with poorer outcomes in cardiac surgery, but the heterogeneity in frailty assessment tools makes it difficult to ascertain its true impact in cardiac surgery. Slow gait speed is a simple, validated, and reliable marker of frailty. We performed a systematic review and meta-analysis to examine the effect of slow gait speed on postoperative cardiac surgical patients. Methods: PubMED, MEDLINE, and EMBASE databases were searched from January 2000 to August 2021 for studies comparing slow gait speed and “normal” gait speed. The primary outcome was in-hospital mortality. Secondary outcomes were composite mortality and major morbidity, AKI, stroke, deep sternal wound infection, prolonged ventilation, discharge to a healthcare facility, and ICU length of stay. Results: There were seven eligible studies with 36,697 patients. Slow gait speed was associated with an increased likelihood of in-hospital mortality (risk ratio [RR]: 2.32; 95% confidence interval [CI]: 1.87–2.87). Additionally, they were more likely to suffer from composite mortality and major morbidity (RR: 1.52; 95% CI: 1.38–1.66), AKI (RR: 2.81; 95% CI: 1.44–5.49), deep sternal wound infection (RR: 1.77; 95% CI: 1.59–1.98), prolonged ventilation >24 h (RR: 1.97; 95% CI: 1.48–2.63), reoperation (RR: 1.38; 95% CI: 1.05–1.82), institutional discharge (RR: 2.08; 95% CI: 1.61–2.69), and longer ICU length of stay (MD: 21.69; 95% CI: 17.32–26.05). Conclusion: Slow gait speed is associated with poorer outcomes in cardiac surgery. Frail patients are twofold more likely to die during hospital admission than non-frail counterparts and are at an increased risk of developing various perioperative complications.

Keywords: cardiac surgery, gait speed, recovery, frailty

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