Search results for: onsite and online learning

Authors: David Ming Liu, Benjamin Hirsch, Bashir Aden

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Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved.

Keywords: Syndromic surveillance, Tweets, Machine Learning, data mining, Latent Dirichlet allocation (LDA), Influenza

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Authors: Fahad Al Beyahi, Justin Thomas, Md Mamunur Rashid

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Various definitions of digital well-being have emerged in recent years, most of which center on the impacts -beneficial and detrimental- of digital technology on health and well-being (psychological, social, and financial). Other definitions go further, emphasizing the attainment of balance, viewing digital well-being as wholly subjective, the individual’s perception of optimal balance between the benefits and ills associated with online connectivity. Based on this broad conceptualization of digital well-being, we undertook a global survey measuring various dimensions of this emerging construct. The survey was administered across 35 nations and 7 world regions, with 1000 participants within each territory (N= 35000). Along with attitudinal, behavioral, and sociodemographic variables, the survey included measures of depression, anxiety, problematic social media use, gaming disorder, and other relevant metrics. Coupled with nation-level policy audits, these data were used to create a multinational (global) digital well-being index. Nations are ranked based on various dimensions of digital well-being, and predictive models are used to identify resilience and risk factors for problem technology use. In this paper, we will discuss key findings from the survey and the index. This work can inform public policy and shape our responses to the emerging implications of lives increasingly lived online and interconnected with digital technology.

Keywords: technology, health, behavioral addiction, digital wellbeing

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Authors: Giovanni Cicceri, Roberta Maisano, Nathalie Morey, Salvatore Distefano

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The main goal of this paper is to present a solution for a water purification system based on an Environmental Internet of Things (EIoT) platform to monitor and control water quality and machine learning (ML) models to support decision making and speed up the processes of purification of water. A real case study has been implemented by deploying an EIoT platform and a network of devices, called Gramb meters and belonging to the Gramb project, on wastewater purification systems located in Calabria, south of Italy. The data thus collected are used to control the wastewater quality, detect anomalies and predict the behaviour of the purification system. To this extent, three different statistical and machine learning models have been adopted and thus compared: Autoregressive Integrated Moving Average (ARIMA), Long Short Term Memory (LSTM) autoencoder, and Facebook Prophet (FP). The results demonstrated that the ML solution (LSTM) out-perform classical statistical approaches (ARIMA, FP), in terms of both accuracy, efficiency and effectiveness in monitoring and controlling the wastewater purification processes.

Keywords: environmental internet of things, EIoT, machine learning, anomaly detection, environment monitoring

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Authors: Liza Mariah Hj. Azahari

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This paper will look at a case study on English Camp which was an activity coordinated at the Sultan Sharif Ali Islamic University in 2011. English Camp is a fun and motivation filled activity which brings students and teachers together outside of the classroom setting into a more diverse environment. It also enables teacher and students to gain proximate time together for a mutual purpose which is to explore the language in a more dynamic and relaxed way. First of all, the study will look into the background of English Camp, and how it was introduced and implemented from different contexts. Thereafter, it will explain the objectives of the English Camp coordinated at our university, UNISSA, and what types of activities were conducted. It will then evaluate the effectiveness of the camp as to what extent it managed to meet its motto, which was to foster dynamic interactive learning of English Language. To conclude, the paper presents a potential for further research on the topic as well as a guideline for educators who wish to coordinate the activity. Proposal for collaboration in this activity is further highlighted and encouraged within the paper for future implementation and endeavor.

Keywords: English camp, UNISSA, interactive learning, outside

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Authors: Maisyatus Suadaa Irfana, Nove Eka Variant Anna, Dyah Puspitasari Sri Rahayu

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Technological developments provide convenience to all the people. Nowadays, the communication of human with the computer is done via text. With the development of technology, human and computer communications have been conducted with a voice like communication between human beings. It provides an easy facility for many people, especially those who have special needs. Voice search technology is applied in the search of book collections in the OPAC (Online Public Access Catalog), so library visitors will find it faster and easier to find books that they need. Integration with Google is needed to convert the voice into text. To optimize the time and the results of searching, Server will download all the book data that is available in the server database. Then, the data will be converted into JSON format. In addition, the incorporation of some algorithms is conducted including Decomposition (parse) in the form of array of JSON format, the index making, analyzer to the result. It aims to make the process of searching much faster than the usual searching in OPAC because the data are directly taken to the database for every search warrant. Data Update Menu is provided with the purpose to enable users perform their own data updates and get the latest data information.

Keywords: OPAC, voice, searching, faster

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Authors: Amit Kumar, Amanpreet Chander, Ashish Sahani

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This paper presents our recent work on real-time human activity detection based on the media pipe pipeline and machine learning algorithms. The proposed system can detect human activities, including running, jumping, squatting, bending to the left or right, and standing still. This is a robust solution for developing a yoga, dance, metaverse, and fitness application that checks for the correction of the pose without having any additional monitor like a personal trainer. MediaPipe solution offers an open-source cross-platform which utilizes a two-step detector-tracker ML pipeline for live detection of key landmarks on our body which can be used for motion data collection. The prediction of real-time poses uses a variety of machine learning techniques and different types of analysis. Without primarily relying on powerful desktop environments for inference, our method achieves real-time performance on the majority of contemporary mobile phones, desktops/laptops, Python, or even the web. Experimental results show that our method outperforms the existing method in terms of accuracy and real-time capability, achieving an accuracy of 99.92% on testing datasets.

Keywords: human activity detection, media pipe, machine learning, metaverse applications

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the model-agnostic meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to an exponential growth of computation, this paper also proposes a key data extraction method that only extract part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: mutex task generation, data augmentation, meta-learning, text classification.

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Authors: Ali Nadi, Ali Edrissi

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Relief demand and transportation links availability is the essential information that is needed for every natural disaster operation. This information is not in hand once a disaster strikes. Relief demand and network condition has been evaluated based on prediction method in related works. Nevertheless, prediction seems to be over or under estimated due to uncertainties and may lead to a failure operation. Therefore, in this paper a stochastic programming model is proposed to evaluate real-time relief demand and network condition at the onset of a natural disaster. To address the time sensitivity of the emergency response, the proposed model uses reinforcement learning for optimization of the total relief assessment time. The proposed model is tested on a real size network problem. The simulation results indicate that the proposed model performs well in the case of collecting real-time information.

Keywords: disaster management, real-time demand, reinforcement learning, relief demand

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Oleg Vlasovets, Fabian Schaipp, Christian L. Mueller

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we conduct a network analysis and predict the sex of 1000 participants based on ”connectome” - pairwise Pearson’s correlation across 436 brain parcels. We solve the non-smooth convex optimization problem, known under the name of Graphical Lasso, where the solution includes a low-rank component. With this solution and machine learning model for a sex prediction, we explain the brain parcels-sex connectivity patterns.

Keywords: network analysis, neuroscience, machine learning, optimization

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Authors: Boon Yih Mah

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Web-based Cognitive Writing Instruction (WeCWI) is a hybrid e-framework that consolidates instructional design and language development towards the development of a web-based instruction (WBI). WeCWI divides instructional design into macro and micro perspectives. In macro perspective, a 21st century educator is encouraged to disseminate knowledge and share ideas with in-class and global learners. By leveraging the virtue of technology, WeCWI aims to transform the educator into an aggregator, curator, publisher, social networker and finally, a web-based instructor. Since the most notable contribution of integrating technology is being a tool of teaching as well as a stimulus for learning, WeCWI focuses on the use of contemporary web tools based on the multiple roles played by the 21st century educator. The micro perspective draws attention to the pedagogical approaches focussing on three main aspects: reading, discussion, and writing. With the effective use of pedagogical approaches, technology adds new dimensions and expands the bounds of learning capacity. Lastly, WeCWI also imparts the fundamental theoretical concepts for web-based instructors’ awareness such as interactionism, e-learning interactional-based model, computer-mediated communication (CMC), cognitive theories, and learning style model.

Keywords: web-based cognitive writing instruction, WeCWI, instructional design, e-framework, web-based instructor

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Authors: Clare Maxwell, Lorna Porcellato, Valerie Fleming, Kate Fleming

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A review of global online forums and social media reveals large numbers of mothers experiencing bottle refusal by their breastfed baby. It is difficult to determine precise numbers due to a lack of data, however, established virtual communities illustrate thousands of posts in relation to the issue. Mothers report various negative consequences of bottle refusal including delaying their return to work, time and financial outlay spent on methods to overcome it and experiencing stress, anxiety, and resentment of breastfeeding. A search of the literature revealed no studies being identified, and due to a lack of epidemiological data, a study investigating mother’s experiences of bottle refusal by their breastfed baby was undertaken. The aim of the study was to investigate UK mothers’ experiences of bottle refusal by their breastfed baby. Data were collected using an online questionnaire collecting quantitative and qualitative data. 841 UK mothers who had experienced or were experiencing bottle refusal by their breastfed baby completed the questionnaire. Data were analyzed using descriptive statistics and non-parametric testing. The results showed 61% (516/840) of mothers reported their breastfed baby was still refusing/had never accepted a bottle, with 39% (324/840) reporting their baby had eventually accepted. The most frequently reported reason to introduce a bottle was so partner/family could feed the baby 59% (499/839). 75% (634/841) of mothers intended their baby to feed on a bottle ‘occasionally’. Babies who accepted a bottle were more likely to be older at 1st attempt to introduce one than those babies who refused (Mdn = 12 weeks v 8 weeks, n = 286) (p = <0.001). Length of time taken to acceptance was 9 weeks (Mdn = 9, IQR = 18, R = 103.9, n = 306) with the older the baby was at 1st attempt to introduce a bottle being associated with a shorter length of time to acceptance (p = < 0.002). 60% (500/841) of mothers stated that none of the methods they used had worked. 26% (222/841) of mothers reported bottle refusal had had a negative impact upon their overall breastfeeding experience. 47% (303/604) reported they would have tried to introduce a bottle earlier to prevent refusal. This study provides a unique insight into the scenario of bottle refusal by breastfed babies. It highlights that bottle refusal by breastfed babies is a significant issue, which requires recognition from those communicating breastfeeding information to mothers.

Keywords: bottle feeding, bottle refusal, breastfeeding, infant feeding

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Authors: Racidon P. Bernarte, Vincent Jude G. Estella, Dominador Jr. M. Nucon, Jin Danniel O. Villatema

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Purpose: Internet pornography has many negative effects, but one of the disturbing phases of pornography usage is; users are insentient on how pornography influences and affects them. The acceptance of Internet pornography use in a relationship has been found to be higher among men than among women. The use of pornography directly correlates to a decrease in sexual intimacy. Hence, this might lead to the weakening of the relationship of the married individuals to their partner. To find out the relevance of the claim, the researchers aimed to explore the relationship of Internet pornography consumption to the relationship commitment of married individuals in the Philippines. Different factors such as level of satisfaction, the size of the investment, quality of alternatives, relationship stability, and viewing habits of the Filipino married individuals were also considered in determining the relationship of watching pornography online and the relationship commitment of the Filipino married individuals. Design/ Methodology/ Approach –The study used the quantitative research approach, specifically descriptive method and correlation in order to further analyze the gathered data. A self-administered survey was distributed to 400 selected Filipino married individuals who were married individuals that are watching pornography on the Internet who are living in Quezon City. Findings –It is revealed that Internet pornography consumption has a negative effect on the relationship commitment of married individuals. Furthermore, watching pornography online weakened the relationship commitment of the Filipino married individuals that leads to an unstable relationship.

Keywords: internet pornography consumption, relationship commitment, married individuals, polytechnic university of the Philippines

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Authors: Md. Shaiful Islam, Mahani Bt. Stapa

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To help learners overcome their struggle in developing proficiency in spoken English, self-regulated learning strategies seem to be promising. Students in the private universities in Bangladesh are expected to communicate with the teachers, peers, and staff members in English, but most of them suffer from their inadequate oral communicative competence in English. To address this problem, the researchers adopted a qualitative research approach to answer the research questions. They employed the learner diary method to collect data from the first-semester undergraduate students of a reputed private university in Bangladesh who were involved in writing weekly diaries about their use of self-regulated learning strategies to improve speaking in an English speaking course. The learners were provided with prompts for writing the diaries. The thematic analysis method was applied to analyze the entries of the diaries for the identification of themes. Seven strategies related to the effectiveness of SRL for the improvement of spoken English were identified from the data, and they include goal-setting, strategic planning, identifying the sources of self-motivation, help-seeking, environmental restructuring, self-monitoring, and self-evaluation. However, the students reported in their diaries that they faced challenges that impeded their SRL strategy use. Five challenges were identified, and they entail the complex nature of SRL, lack of literacy on SRL, teachers’ preference for controlling the class, learners’ past habit of learning, and students’ addiction to gadgets. The implications the study addresses include revising the syllabus and curriculum, facilitating SRL training for students and teachers, and integrating SRL in the lessons.

Keywords: private university in Bangladesh, proficiency, self-regulated learning, spoken English

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Authors: Ghazi Alotaibi

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The current study examined and described the views of secondary school students and their parents on their reasons for seeking private tutors in English. These views were obtained through two group interviews with the students and parents separately. Several causes were brought up during the two interviews. These causes included difficulty of the English language, weak teacher performance, the need to pass exams with high marks, lack of parents’ follow-up of student school performance, social pressure, variability in student comprehension levels at school, weak English foundation in previous school years, repeated student absence from school, large classes, as well as English teachers’ heavy teaching loads. The study started with a description of the EFL educational system in Saudi Arabia and concluded with recommendations for the improvement of the school learning environment.

Keywords: english, learning difficulty, private tutoring, Saudi, teaching practices, learning environment

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Authors: Migena Mana, Ahmed Khalid Syed, Abdul Malik, Nikhil Cherian

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In recent years, advancements in deep learning enabled researchers to tackle the problem of self-driving cars. Car companies use huge datasets to train their deep learning models to make autonomous cars a reality. However, this approach has certain drawbacks in that the state space of possible actions for a car is so huge that there cannot be a dataset for every possible road scenario. To overcome this problem, the concept of reinforcement learning (RL) is being investigated in this research. Since the problem of autonomous driving can be modeled in a simulation, it lends itself naturally to the domain of reinforcement learning. The advantage of this approach is that we can model different and complex road scenarios in a simulation without having to deploy in the real world. The autonomous agent can learn to drive by finding the optimal policy. This learned model can then be easily deployed in a real-world setting. In this project, we focus on three RL algorithms: Q-learning, Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO). To model the environment, we have used TORCS (The Open Racing Car Simulator), which provides us with a strong foundation to test our model. The inputs to the algorithms are the sensor data provided by the simulator such as velocity, distance from side pavement, etc. The outcome of this research project is a comparative analysis of these algorithms. Based on the comparison, the PPO algorithm gives the best results. When using PPO algorithm, the reward is greater, and the acceleration, steering angle and braking are more stable compared to the other algorithms, which means that the agent learns to drive in a better and more efficient way in this case. Additionally, we have come up with a dataset taken from the training of the agent with DDPG and PPO algorithms. It contains all the steps of the agent during one full training in the form: (all input values, acceleration, steering angle, break, loss, reward). This study can serve as a base for further complex road scenarios. Furthermore, it can be enlarged in the field of computer vision, using the images to find the best policy.

Keywords: autonomous driving, DDPG (deep deterministic policy gradient), PPO (proximal policy optimization), reinforcement learning

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Authors: Anat Peleg, Hadar Dancig-Rosenberg

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This interdisciplinary study, to our knowledge the first in this field, is located on the intersection of victimology-law and society-and media literature, and it corresponds both with feminist writing and with cyber literature which explores the techno-social sphere. It depicts the multifaceted dimensions of shaming in the eyes of the survivors through the following research questions: What are the motivations of sexual-assault survivors to publicize the assailants' identity or to refrain from this practice? Is shaming on Facebook perceived by sexual–assault victims as a substitute for the CJS or as a new form of social activism? What positive and negative consequences do survivors experience as a result of shaming their assailants online? The study draws on in-depth semi-structured interviews which we have conducted between 2016-2018 with 20 sexual-assaults survivors who exposed themselves on Facebook. They were sexually attacked in various forms: six participants reported that they had been raped when they were minors; eight women reported that they had been raped as adults; three reported that they had been victims of an indecent act and three reported that they had been harassed either in their workplace or in the public sphere. Most of our interviewees (12) reported to the police and were involved in criminal procedures. More than half of the survivors (11) disclosed the identity of their attackers online. The vocabularies of motives that have emerged from the thematic analysis of the interviews with the survivors consist of both social and personal motivations for using the practice of shaming online. Some survivors maintain that the use of shaming derives from the decline in the public trust in the criminal justice system. It reflects demand for accountability and justice and serves also as a practice of warning other potential victims of the assailants. Other survivors assert that shaming people in a position of privilege is meant to fulfill the public right to know who these privileged men really are. However, these aforementioned moral and practical justifications of the practice of shaming are often mitigated by fear from the attackers' physical or legal actions in response to their allegations. Some interviewees who are feminist activists argue that the practice of shaming perpetuates the social ancient tendency to define women by labels linking them to the men who attacked them, instead of being defined by their own life complexities. The variety of motivations to adopt or resent the practice of shaming by sexual assault victims presented in our study appear to refute the prevailing intuitive stereotype that shaming is an irrational act of revenge, and denote its rationality. The role of social media as an arena for seeking informal justice raises questions about the new power relations created between victims, assailants, the community and the State, outside the formal criminal justice system. At the same time, the survivors' narratives also uncover the risks and pitfalls embedded within the online sphere for sexual assault survivors.

Keywords: criminal justice, gender, Facebook, sexual-assaults

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Authors: Mehul Srivastava, Sharma Tushar Ravikant, Mridul Krishn Mishra

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In structural design, the evaluation of safety and probability failure of structure is of significant importance, mainly when the variables are random. On real structures, structural reliability can be evaluated obtaining an implicit limit state function. The structural reliability limit state function is obtained depending upon the statistically independent variables. In the analysis of reliability, we considered the statistically independent random variables to be the load intensity applied and the depth or height of the beam member considered. There are many approaches for structural reliability problems. In this paper Extreme Learning Machine technique and First Order Second Moment Method is used to determine the reliability indices for the same set of variables. The reliability index obtained using ELM is compared with the reliability index obtained using FOSM. Higher the reliability index, more feasible is the method to determine the reliability.

Keywords: reliability, reliability index, statistically independent, extreme learning machine

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Authors: Sakae Suzuki

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Although Japanese students study English for six years in secondary schools, they demonstrate little success with it when they enter higher education. Learners’ beliefs can predict the future behavior of students, so it may be effective to investigate how learners’ beliefs limit their success and how beliefs might be nudged in a positive direction. While many researchers still depend on a questionnaire called BALLI to reveal explicit beliefs, alternative approaches, especially those designed to reveal implicit beliefs, might be helpful for promoting learning. The present study seeks to identify beliefs with a discursive approach using visual metaphors and narratives. Employing a sociocultural framework, this study investigates how students’ beliefs are revealed by drawings of themselves and their surrounding environments and artifacts while they are engaged in language learning. Research questions are: (1) Can we identify beliefs through an analysis of students’ visual narratives? (2) What environments and artifacts can be found in students’ drawings, and what do they mean? (3) To what extent do students see language learning as a solitary, rather than a social, activity? Participants are university students majoring in science and technology in Japan. The questionnaire was administered to 70 entering students in April, 2014. Data included students drawings of themselves as learners of English as well as written descriptions of students’ backgrounds, English-learning experiences, and analogies and metaphors that they used in written descriptions of themselves as learners. Data will be analyzed qualitatively and quantitatively. Anticipated results include students’ perceptions of themselves as language learners, including their sense of agency, awareness of artifacts, and social contexts of language learning. Comments will be made on implications for teaching, as well as the use of visual narratives as research tools, and recommended further research.

Keywords: drawings, learners' beliefs, metaphors, BALLI

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Authors: Jeongmin Kim, Eunju Lee, Kwang Ryel Ryu

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This paper addresses the problem of predictive control for energy management of large-scaled exhibition halls, where a lot of energy is consumed to maintain internal atmosphere under certain required conditions. Predictive control achieves better energy efficiency by optimizing the operation of air-conditioning facilities with not only the current but also some future status taken into account. In this paper, we propose to use predictive models learned from past sensor data of hall environment, for use in optimizing the operating plan for the air-conditioning facilities by simulating future environmental change. We have implemented an emulator of an exhibition hall by using EnergyPlus, a widely used building energy emulation tool, to collect data for learning environment-change models. Experimental results show that the learned models predict future change highly accurately on a short-term basis.

Keywords: predictive control, energy management, machine learning, optimization

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Authors: Fahd Hamad Alqasham

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This longitudinal study investigates Saudi students’ use trajectory and experiences of Twitter as an innovative tool for in-class learning of the English language in a Saudi tertiary English as a foreign language (EFL) context for a 12-week semester. The study adopted van Lier’s agency theory (2008, 2010) as the analytical framework to obtain an in-depth analysis of how the learners’ could utilize Twitter to create innovative ways for them to engage in English learning inside the language classroom. The study implemented a mixed methods approach, including six data collection instruments consisting of a research log, observations, focus group participation, initial and post-project interviews, and a post-project questionnaire. The study was conducted at Qassim University, specifically at Preparatory Year Program (PYP) on the main campus. The sample included 25 male students studying in the first level of PYP. The findings results revealed that although Twitter’s affordances initially paled a crucial role in motivating the learners to initiate their agency inside the classroom to learn English, the contextual constraints, mainly anxiety, the university infrastructure, and the teacher’s role negatively influenced the sustainability of Twitter’s use past week nine of its implementation.

Keywords: CALL, agency, innovation, EFL, language learning

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Authors: Christopher Webb

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This work presents a new model of mentoring in an organizational environment and has important implications for both practice and research, the model frames the organizational environment as organizational curriculum, which includes the elements that affect learning within the organization. This includes the organizational structure and culture, roles within the organization, and accessibility of knowledge. The program curriculum includes the elements of the mentoring program, including materials, training, and scheduled events for the program participants. The term dyadic curriculum is coined in this work. The dyadic curriculum describes the participation, behavior, and identities of the pairs participating in mentorships. This also includes the identity work of the participants and their views of each other. Much of this curriculum is unprescribed and is unique within each dyad. It describes how participants mediate the elements of organizational and program curricula. These three curricula interact and affect each other in predictable ways. A detailed example of a mentoring program framed in this model is provided.

Keywords: curriculum, mentoring, organizational learning and development, social learning

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Authors: Sindile Ngubane-Mokiwa

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This paper addresses two questions: (1) what barriers do the blind students face with assessment and feedback in open distance learning contexts? And (2) How can these barriers be removed? The paper focuses on the distance education through which most students with disabilities elevate their chances of accessing higher education. Lack of genuine inclusion is also evident in the challenges the blind students face during the assessment. These barriers are experienced at both formative and summative stages. The insights in this paper emanate from a case study that was carried out through qualitative approaches. The data was collected through in-depth interview, life stories, and telephonic interviews. The paper provides a review of local, continental and international views on how best assessment barriers can be removed. A group of five blind students, comprising of two honours students, two master's students and one doctoral student participated in this study. The data analysis was done through thematic analysis. The findings revealed that (a) feedback to the assignment is often inaccessible; (b) the software used is incompatible; (c) learning and assessment are designed in exclusionary approaches; (d) assessment facilities are not conducive; and (e) lack of proactive innovative assessment strategies. The article concludes by recommending ways in which barriers to assessment can be removed. These include addressing inclusive assessment and feedback strategies in professional development initiatives.

Keywords: assessment design, barriers, disabilities, blind students, feedback, universal design for learning

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Authors: T. Hopkin, S. Lu, P. Rogers, M. Sexton

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Housing Associations (HAs) contribute circa 20% of the UK’s housing supply. HAs are however under increasing pressure as a result of funding cuts and rent reductions. Due to the increased pressure, a number of processes are currently being reviewed by HAs, especially how they manage and learn from defects. Learning from defects is considered a useful approach to achieving defect reduction within the UK housebuilding industry. This paper contributes to our understanding of how HAs learn from defects by undertaking an initial round table discussion with key HA stakeholders as part of an ongoing collaborative research project with the National House Building Council (NHBC) to better understand how house builders and HAs learn from defects to reduce their prevalence. The initial discussion shows that defect information runs through a number of groups, both internal and external of a HA during both the defects management process and organizational learning (OL) process. Furthermore, HAs are reliant on capturing and recording defect data as the foundation for the OL process. During the OL process defect data analysis is the primary enabler to recognizing a need for a change to organizational routines. When a need for change has been recognized, new options are typically pursued to design out defects via updates to a HAs Employer’s Requirements. Proposed solutions are selected by a review board and committed to organizational routine. After implementing a change, both structured and unstructured feedback is sought to establish the change’s success. The findings from the HA discussion demonstrates that OL can achieve defect reduction within the house building sector in the UK. The paper concludes by outlining a potential ‘learning from defects model’ for the housebuilding industry as well as describing future work.

Keywords: defects, new homes, housing association, organizational learning

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Authors: Catherine Chua, Kashif Raza

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Work-integrated learning (WIL) has been heightened in the last few years across countries. With a specific attention on working adults, the key objective is to integrate work experiences with academic studies so that they will be given more opportunities to advance, gather relevant skills and credentials to enable them to contribute more positively to the labour market. In Singapore, developing talent through WIL aims to develop specialist and enduring skills for the industries. Collaborating with the institutes of higher education in Singapore, the Integrated Work Study Programs (IWSP) seek to harmonize classroom learning with practical work experiences so that adult students can develop skills and knowledge that are needed in the existing and future workplaces. Local higher education institutions will also work closely with industry partners, and design courses that support these students to deepen their skills. Using Critical Discourse Analysis, this paper examines the Singapore government policies in WIL and argues that despite the various supports and interventions provided by the government, it is equally important to create a ‘space’ in the society whereby there is a greater recognition for WIL as a valuable education approach, i.e., “continuous meritocracy”. This is especially so in Singapore where academic excellence and conventional front-loaded approach to education are valued.

Keywords: work-integrated learning, adult learners, continuous meritocracy, skillsfuture singapore

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Authors: Sitalakshmi Venkatraman, Fiona Wahr, Anthony de Souza-Daw, Samuel Kaspi

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There is a growing emphasis on generic skills in higher education to match the changing skill-set requirements of the labour market. However, researchers and policy makers have not arrived at a consensus on the generic skills that actually contribute towards workplace employability and performance that complement and/or underpin discipline-specific graduate attributes. In order to strengthen the qualifications framework, a range of ‘generic’ learning outcomes have been considered for students undergoing higher education programs and among them it is necessary to have the fundamental generic skills such as literacy and numeracy at a level appropriate to the qualification type. This warrants for curriculum design approaches to contextualise the form and scope of these fundamental generic skills for supporting both students’ learning engagement in the course, as well as the graduate attributes required for employability and to progress within their chosen profession. Little research is reported in integrating such generic skills into discipline-specific learning outcomes. This paper explores the literature of the generic skills required for graduates from the discipline of Information Technology (IT) in relation to an Australian higher education institution. The paper presents the rationale of a proposed Bachelor of IT curriculum designed to contextualize the learning of these generic skills within the students’ discipline studies.

Keywords: curriculum, employability, generic skills, graduate attributes, higher education, information technology

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Authors: Ganfeng Yu, Yuefeng Ma, Shanliang Yang

Abstract:

The word discovery is a key problem in text information retrieval technology. Methods in new word discovery tend to be closely related to words because they generally obtain new word results by analyzing words. With the popularity of social networks, individual netizens and online self-media have generated various network texts for the convenience of online life, including network words that are far from standard Chinese expression. How detect network words is one of the important goals in the field of text information retrieval today. In this paper, we integrate the word embedding model and clustering methods to propose a network word discovery framework based on sentence semantic similarity (S³-NWD) to detect network words effectively from the corpus. This framework constructs sentence semantic vectors through a distributed representation model, uses the similarity of sentence semantic vectors to determine the semantic relationship between sentences, and finally realizes network word discovery by the meaning of semantic replacement between sentences. The experiment verifies that the framework not only completes the rapid discovery of network words but also realizes the standard word meaning of the discovery of network words, which reflects the effectiveness of our work.

Keywords: text information retrieval, natural language processing, new word discovery, information extraction

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Authors: Gholizadeh F. F., Goldansaz S. H., Bandani A. R., A. Ashouri

Abstract:

This study aimed to investigate the direct effects of increasing carbon dioxide (CO₂) concentration on the behavior of Bombus terrestris bumblebees in laboratory conditions to understand the outcomes of the augmentation of this gas in the Earth's atmosphere on the decline of populations of these pollinators. Learning and memory duration of bumblebees were evaluated as two main behavioral factors in social insects at different concentrations of CO₂. In both series of experiments, the behavior of bees under the influence of CO₂ changes compared to the control. Insects kept at high CO₂ concentrations learn less than control bees and spend more time identifying and navigating to discover their food source and access time (nectar consumption). These results showed that bees maybe lose some of their food resources due to poorer identification and act weaker on searching due to less memory and avoiding the enemy in higher CO₂ concentration. Therefore, CO₂ increasing concentration can be one of the reasons for the decline of these pollinating insects' populations by negatively affecting their fitness.

Keywords: Bombus terrestris, CO₂, learning, memory duration

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Authors: Yen-Hui Lu

Abstract:

In the past two decades, in order to increase university visibility and internationalization, English as a medium of instruction (EMI) has become one of the main language policies in higher education institutions where English is not a dominant language. However, given the complex, discipline-embedded nature of academic communication, academic literacy does not come with students’ everyday language experience, and it is a challenge for all students. Particularly, to engage students in the effective learning process of discipline concepts in the EMI classrooms, teachers need to provide explicit academic language instruction to assist students in deep understanding of discipline concepts. To bridge the gap between academic language development and discipline learning in the EMI classrooms, the researcher incorporates academic language strategies and key elements of project-based learning (PBL) into an Academic Language Strategy driven PBL (ALS-PBL) model. With clear steps and strategies, the model helps EMI teachers to scaffold students’ academic language development in the EMI classrooms. ALS-PBL model includes three major stages: preparation, implementation, and assessment. First, in the preparation stage, ALS-PBL teachers need to identify learning goals for both content and language learning and to design PBL topics for investigation. Second, during the implementation stage, ALS-PBL teachers use the model as a guideline to create a lesson structure and class routine. There are five important elements in the implementation stage: (1) academic language preparation, (2) connecting background knowledge, (3) comprehensible input, (4) academic language reinforcement, and (5) sustained inquiry and project presentation. Finally, ALS-PBL teachers use formative assessments such as student learning logs, teachers’ feedback, and peer evaluation to collect detailed information that demonstrates students’ academic language development in the learning process. In this study, ALS-PBL model was implemented in an interdisciplinary course entitled “Science is Everywhere”, which was co-taught by five professors from different discipline backgrounds, English education, civil engineering, business administration, international business, and chemical engineering. The purpose of the course was to cultivate students’ interdisciplinary knowledge as well as English competency in disciplinary areas. This study used a case-study design to systematically investigate students’ learning experiences in the class using ALS-PBL model. The participants of the study were 22 college students with different majors. This course was one of the elective EMI courses in this focal university. The students enrolled in this EMI course to fulfill the school language policy, which requires the students to complete two EMI courses before their graduation. For the credibility, this study used multiple methods to collect data, including classroom observation, teachers’ feedback, peer assessment, student learning log, and student focus-group interviews. Research findings show four major successful aspects of implementing ALS-PBL model in the EMI classroom: (1) clear focus on both content and language learning, (2) meaningful practice in authentic communication, (3) reflective learning in academic language strategies, and (4) collaborative support in content knowledge.This study will be of value to teachers involved in delivering English as well as content lessons to language learners by providing a theoretically-sound practical model for application in the classroom.

Keywords: academic language development, content and language integrated learning, english as a medium of instruction, project-based learning

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Authors: Jiaqi Huang, Yuheng Wang

Abstract:

Premises selection, the task of selecting a set of axioms for proving a given conjecture, is a major bottleneck in automated theorem proving. An array of deep-learning-based methods has been established for premises selection, but a perfect performance remains challenging. Our study examines the inaccuracy of deep neural networks in premises selection. Through training network models using encoded conjecture and axiom pairs from the Mizar Mathematical Library, two potential biases are found: the network models classify more premises as necessary than unnecessary, referred to as the ‘positive bias’, and the network models perform better in proving conjectures that paired with more axioms, referred to as ‘length bias’. The ‘positive bias’ and ‘length bias’ discovered could inform the limitation of existing deep neural networks.

Keywords: automated theorem proving, premises selection, deep learning, interpreting deep learning

Procedia PDF Downloads 175