Search results for: student performance prediction

Authors: Jamerson Felipe Pereira Lima, Jeane Cecília Bezerra de Melo

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The difficulty and cost related to obtaining of protein tertiary structure information through experimental methods, such as X-ray crystallography or NMR spectroscopy, helped raising the development of computational methods to do so. An approach used in these last is prediction of tridimensional structure based in the residue chain, however, this has been proved an NP-hard problem, due to the complexity of this process, explained by the Levinthal paradox. An alternative solution is the prediction of intermediary structures, such as the secondary structure of the protein. Artificial Intelligence methods, such as Bayesian statistics, artificial neural networks (ANN), support vector machines (SVM), among others, were used to predict protein secondary structure. Due to its good results, artificial neural networks have been used as a standard method to predict protein secondary structure. Recent published methods that use this technique, in general, achieved a Q3 accuracy between 75% and 83%, whereas the theoretical accuracy limit for protein prediction is 88%. Alternatively, to achieve better results, support vector machines prediction methods have been developed. The statistical evaluation of methods that use different AI techniques, such as ANNs and SVMs, for example, is not a trivial problem, since different training sets, validation techniques, as well as other variables can influence the behavior of a prediction method. In this study, we propose a prediction method based on artificial neural networks, which is then compared with a selected SVM method. The chosen SVM protein secondary structure prediction method is the one proposed by Huang in his work Extracting Physico chemical Features to Predict Protein Secondary Structure (2013). The developed ANN method has the same training and testing process that was used by Huang to validate his method, which comprises the use of the CB513 protein data set and three-fold cross-validation, so that the comparative analysis of the results can be made comparing directly the statistical results of each method.

Keywords: artificial neural networks, protein secondary structure, protein structure prediction, support vector machines

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Authors: Pratheesh Jakkala

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This paper addresses the use of breakout sessions to conduct successful weekly physics recitations for College Physics I and II at a large University in remote teaching method during COVID-19 pandemic. All breakout sessions are synchronous, conducted live, and handled by teaching assistants. A two-prong approach is used to maintain the integrity of recitations. Three different conference platforms WebEx, Zoom, and Canvas conferences, were tested, and BigBlue button using Canvas was adopted. The results and experiences of all three learning platforms are presented in this paper. Recitation questions were assigned on WebAssign learning platform and a standard five-question template developed by the instructor was used for group discussions and active peer-peer engagement. Breakout sessions feature of BigBlueButton in Canvas conferences was successfully implemented. Each breakout session consists of a team of 4 students. An online whiteboard, chat window options were used for live teamwork. Student peer-peer interactions, Teaching Assistants’ interaction with students were video and audio recorded. A total of 72 students in College Physics II and 55 students in College Physics I was enrolled. 82% of students agreed that method under study is better than previous methods. The study addressed the quality of student teamwork, student attitude towards problem-solving, and student performance in the exams.

Keywords: recitations, breakout rooms, online learning platforms, COVID pandemic

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.

Keywords: Levy flight, distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

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Authors: Abdullah Al Farwan, Ya Zhang

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In today’s educational arena, it is critical to understand educational data and be able to evaluate important aspects, particularly data on student achievement. Educational Data Mining (EDM) is a research area that focusing on uncovering patterns and information in data from educational institutions. Teachers, if they are able to predict their students' class performance, can use this information to improve their teaching abilities. It has evolved into valuable knowledge that can be used for a wide range of objectives; for example, a strategic plan can be used to generate high-quality education. Based on previous data, this paper recommends employing data mining techniques to forecast students' final grades. In this study, five data mining methods, Decision Tree, JRip, Naive Bayes, Multi-layer Perceptron, and Random Forest with wrapper feature selection, were used on two datasets relating to Portuguese language and mathematics classes lessons. The results showed the effectiveness of using data mining learning methodologies in predicting student academic success. The classification accuracy achieved with selected algorithms lies in the range of 80-94%. Among all the selected classification algorithms, the lowest accuracy is achieved by the Multi-layer Perceptron algorithm, which is close to 70.45%, and the highest accuracy is achieved by the Random Forest algorithm, which is close to 94.10%. This proposed work can assist educational administrators to identify poor performing students at an early stage and perhaps implement motivational interventions to improve their academic success and prevent educational dropout.

Keywords: classification algorithms, decision tree, feature selection, multi-layer perceptron, Naïve Bayes, random forest, students’ academic performance

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Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami

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Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.

Keywords: ANFIS, MGT, prediction modeling, rail track degradation

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Authors: Vijay Kr. Yadav, Nilam Rathi

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Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set.

Keywords: back-propagation, diabetes mellitus, fuzzy inference system, neuro-fuzzy

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Authors: Sheila Santharamohana, Susan Bennett

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Historically, the attrition rates in secondary schools of Indigenous people or Orang Asli of Malaysia have been a cause for nationwide concern. Efforts to increase student engagement focusing on curriculum re-design and aid have not had the targeted impact. The scope of the research explored a change model incorporating project-based learning and wrap-around support through school-community partnerships to increase Orang Asli engagement, student outcomes and improve cultural connectedness. The evaluation methodology was mixed-method comprising a student questionnaire, interviews, and document analysis. Data and evidence were gathered from school staff, community, the Orang Asli governmental authority (JAKOA) and external agencies. Findings from the year-long research suggests shared values and goals in school-community partnerships foster responsive leadership and is key to safeguarding vulnerable Orang Asli, resulting in improved student outcomes. The research highlighted the barriers to the recognition and distinct needs and unique values of the Orang Asli that impact their educational equity and outcomes.

Keywords: Indigenous Education, Cultural Connectedness, School-Community Partnership, Student Outcomes

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Authors: Karl Baker-Green, Ian Woolsey

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Historically, a variety of methods have been used to access the student voice within higher education, including module evaluations and informal classroom feedback. However, currently, the views articulated in student-staff-committee meetings bear the most weight and can therefore have the most significant impact on departmental policy. Arguably, these forums are exclusionary as several students, including those who experience severe anxiety, might feel unable to participate in this face-to-face (large) group activities. Similarly, students who declare a disability, but are not in possession of a learning contract, are more likely to withdraw from their studies than those whose additional needs have been formally recognised. It is also worth noting that whilst the number of disabled students in Higher Education has increased in recent years, the percentage of those who have been issued a learning contract has decreased. These issues foreground the need to explore the educational experiences of students with or without a learning contract in order to identify their respective aspirations and needs and therefore help shape education policy. This is in keeping with the ‘Nothing about us without us’, agenda, which recognises that disabled individuals are best placed to understand their own requirements and the most effective strategies to meet these.

Keywords: education, student voice, student experience, student retention

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Authors: Ravinder Bahl, Jamini Sharma

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The paper demonstrates analysis of the cervical cancer based on a probabilistic model. It involves technique for classification and prediction by recognizing typical and diagnostically most important test features relating to cervical cancer. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases. The combination of the conventional statistical and machine learning tools is applied for the analysis. Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Keywords: cervical cancer, recurrence, no recurrence, probabilistic, classification, prediction, machine learning

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Authors: A. A. Altalbe, N. W Bergmann

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There has been significant recent interest in on-line learning, as well as considerable work on developing technologies for virtual laboratories for engineering students. After reviewing the state-of-the-art of virtual laboratories, this paper steps back from the technology issues to look in more detail at the pedagogical issues surrounding virtual laboratories, and examines the role of gathering student feedback in the development of such laboratories. The main contribution of the paper is a set of student surveys before and after a prototype deployment of a simulation laboratory tool, and the resulting analysis which leads to some tentative guidelines for the design of virtual engineering laboratories.

Keywords: engineering education, elearning, electrical engineering, virtual laboratories

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Authors: Feng Guolin, Gong Zhiqiang

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In order to study the spatial structure and dynamical mechanism of extreme precipitation in East Asia, a corresponding climate network is constructed by employing the method of event synchronization. It is found that the area of East Asian summer extreme precipitation can be separated into two regions: one with high area weighted connectivity receiving heavy precipitation mostly during the active phase of the East Asian Summer Monsoon (EASM), and another one with low area weighted connectivity receiving heavy precipitation during both the active and the retreat phase of the EASM. Besides，a way for the prediction of extreme precipitation is also developed by constructing a directed climate networks. The simulation accuracy in East Asia is 58% with a 0-day lead, and the prediction accuracy is 21% and average 12% with a 1-day and an n-day (2≤n≤10) lead, respectively. Compare to the normal EASM year, the prediction accuracy is lower in a weak year and higher in a strong year, which is relevant to the differences in correlations and extreme precipitation rates in different EASM situations. Recognizing and identifying these effects is good for understanding and predicting extreme precipitation in East Asia.

Keywords: synchronization, climate network, prediction, rainfall

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Authors: D. Hassell, D. De Focatiis

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This paper presents work on the application of wiki based coursework for a fourth-year engineering module delivered as part of both a MEng and MSc programme in Chemical Engineering. The module was taught with an equivalent structure simultaneously on two separate campuses, one in the United Kingdom (UK) and one in Malaysia, and the subsequent results were compared. Student feedback was sought via questionnaires, with 45 respondents from the UK and 49 from Malaysia. Results include discussion on; perceived difficulty; student enjoyment and experiences; differences between MEng and MSc students; differences between cohorts on different campuses. The response of students to the use of wiki-based coursework was found to vary based on their experiences and background, with UK students being generally more positive on its application than those in Malaysia.

Keywords: engineering education, student differences, student learning, web based coursework

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Authors: Haofei Li

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To support academic growth and foster love of learning, MathMates has been introduced for grade 6-8 students at Andrew Hamilton public school in 2022. The program is targeted at students from diverse backgrounds, particularly those underperforming in Pennsylvania System of School Assessment (PSSA) exams. Then, this study aims to evaluate the efficacy of MathMates by comparing student performance on the PSSA test, before and after the intervention. Through a randomized control trial, the study will collect associated costs using the ingredients method and measure the effectiveness for cost-effectiveness analysis. Text messages will be sent to parents/guardians as a reminder of the program and to encourage student participation. The findings of this study will provide valuable insights for funding organizations seeking to understand the impact and costs of math tutoring interventions on student academic achievement, which also emphasizes the importance of the collaborative efforts between higher education and local public schools.

Keywords: mathematics education, mathematics tutoring, college-community initiative, middle schools, Philadelphia public schools, after-school program, PSSA

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Authors: Matthew David Marmet

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Knowing your audience is important regardless of what profession you are in. Whether this audience is comprised of customers or students, having an idea of who these people are, where they come from, and some of the challenges they may have faced allows us to build better relationships with them. This paper will recap a field study experience that has been dubbed a "reverse field trip" to a local high school. Here, going back in time produced not only a great deal of nostalgia, but also served as a reminder of who prospective university students are before they arrive. This information is invaluable as it can help inform classroom (and other) strategies that may help them succeed, and persist through the college years, which will no doubt present them with undeniable changes and challenges. Interviews with school staff and observations of student behavior, both inside and outside the classroom, yielded several lessons learned (i.e., issues to address). These include considerations of regimen, three separate yet related levels of context, and expectation-setting. Each issue will be presented in detail, along with pedagogical strategies to help address them. These strategies have both student-level and institutional benefits as they have the potential not only to increase student engagement, but also improve retention rates.

Keywords: pedagogy, Student engagement, student retention, teaching strategy

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: Mohd Sabri Minhat, Nurul Adilla Mohd Subha

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The PUSPATI TRIGA Reactor (RTP), Malaysia reached its first criticality on June 28, 1982, with power capacity 1MW thermal. The Feedback Control Algorithm (FCA) which is conventional Proportional-Integral (PI) controller, was used for present power control method to control fission process in RTP. It is important to ensure the core power always stable and follows load tracking within acceptable steady-state error and minimum settling time to reach steady-state power. At this time, the system could be considered not well-posed with power tracking performance. However, there is still potential to improve current performance by developing next generation of a novel design nuclear core power control. In this paper, the dual-mode predictions which are proposed in modelling Optimal Model Predictive Control (OMPC), is presented in a state-space model to control the core power. The model for core power control was based on mathematical models of the reactor core, OMPC, and control rods selection algorithm. The mathematical models of the reactor core were based on neutronic models, thermal hydraulic models, and reactivity models. The dual-mode prediction in OMPC for transient and terminal modes was based on the implementation of a Linear Quadratic Regulator (LQR) in designing the core power control. The combination of dual-mode prediction and Lyapunov which deal with summations in cost function over an infinite horizon is intended to eliminate some of the fundamental weaknesses related to MPC. This paper shows the behaviour of OMPC to deal with tracking, regulation problem, disturbance rejection and caters for parameter uncertainty. The comparison of both tracking and regulating performance is analysed between the conventional controller and OMPC by numerical simulations. In conclusion, the proposed OMPC has shown significant performance in load tracking and regulating core power for nuclear reactor with guarantee stabilising in the closed-loop.

Keywords: core power control, dual-mode prediction, load tracking, optimal model predictive control

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

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Authors: Maria Hopkins

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Research on student engagement is founded on a desire to improve the quality of online instruction in both course design and delivery. A high level of student engagement is associated with a wide range of educational practices including purposeful student-faculty contact, peer to peer contact, active and collaborative learning, and positive factors such as student satisfaction, persistence, achievement, and learning. By encouraging student engagement, institutions of higher education can have a positive impact on student success that leads to retention and degree completion. The current research presents the results of an online student engagement survey which support faculty teaching practices to maximize the learning experience for online students. The ‘Indicators of Engaged Learning Online’ provide a framework that measures level of student engagement. Social constructivism and collaborative learning form the theoretical basis of the framework. Social constructivist pedagogy acknowledges the social nature of knowledge and its creation in the minds of individual learners. Some important themes that flow from social constructivism involve the importance of collaboration among instructors and students, active learning vs passive consumption of information, a learning environment that is learner and learning centered, which promotes multiple perspectives, and the use of social tools in the online environment to construct knowledge. The results of the survey indicated themes that emphasized the importance of: Interaction among peers and faculty (collaboration); Timely feedback on assignment/assessments; Faculty participation and visibility; Relevance and real-world application (in terms of assignments, activities, and assessments); and Motivation/interest (the need for faculty to motivate students especially those that may not have an interest in the coursework per se). The qualitative aspect of this student engagement study revealed what instructors did well that made students feel engaged in the course, but also what instructors did not do well, which could inform recommendations to faculty when expectations for teaching a course are reviewed. Furthermore, this research provides evidence for the connection between higher student engagement and persistence and retention in online programs, which supports our rationale for encouraging student engagement, especially in the online environment because attrition rates are higher than in the face-to-face environment.

Keywords: instructional design, learning effectiveness, online learning, student engagement

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Authors: Farzad Hosseini Hossein Abadi, Cristina Prieto Sierra, Cesar Álvarez Díaz

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Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models.

Keywords: LSTMs, streamflow, hyperparameters, hydrology

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: Amiel Satvedi, Richard Pyne

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Injuries in professional sports occur on a regular basis. Some may be minor, while others can cause huge impact on a player's career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player's number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression model.

Keywords: injury predictor, soccer injury prevention, machine learning in soccer, big data in soccer

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Authors: Chhavi Rana, Sanjay K Jain

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All sides of every education debate agree that quality learning happens when knowledgeable, caring teachers use sound pedagogy. Many deliberations of pedagogy make the fault of considering it as principally being about teaching. There has been lot of research about how to build a positive climate for learning, improve student curiosity, and enhance classroom association. However, these things can only be facilitated when teachers are equipped with better teaching techniques that use sound and accurate pedagogy. Pedagogy is the science and art of education. Its aims range from the full development of the human being to skills acquisition. In India, a project named Mission 10 x has been started by an esteemed IT Corporation Wipro as a faculty development programme (FDP) that particularly focus on elements that facilitated teachers in developing curriculum and new pedagogies that can lead to improvement in student engagement. This paper presents a study of these FDPs and examines (1) the parameters that help teachers in building new pedagogies (2) the extent to which appropriate usage of pedagogy is improved after the conduct of Mission 10 x FDPs, and (3) whether institutions differ in terms of their ability to convert usage of improved pedagogy into academic performance via these FDPs. The sample consisted of 2,236 students at 6 four-year engineering colleges and universities that completed several FDPs during 2012-2014. Many measures of usage of better pedagogy were linked positively with such FDPs, although some of the relationships were weak in strength. The results suggest that the usage of pedagogy were more benefited after conducting these FDPs and application of novel approaches in conducting classes.

Keywords: student engagement, critical thinking; achievement, student learning, pedagogy

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Authors: S. Channgam, A. Sae-Tang, T. Termsaithong

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In this research, the occurrences of large size events in various system sizes of the Bak-Tang-Wiesenfeld sandpile model are considered. The system sizes (square lattice) of model considered here are 25×25, 50×50, 75×75 and 100×100. The cross-correlation between the ratio of sites containing 3 grain time series and the large size event time series for these 4 system sizes are also analyzed. Moreover, a prediction method of the large-size event for the 50×50 system size is also introduced. Lastly, it can be shown that this prediction method provides a slightly higher efficiency than random predictions.

Keywords: Bak-Tang-Wiesenfeld sandpile model, cross-correlation, avalanches, prediction method

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Authors: Amal A. Abdel-Sattar

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

Keywords: architecture, building surveying, student-centered learning, teaching and learning

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Authors: Sanja Bracun, Nikolina Kasunic

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After graduation, the student population of applied sciences will become the population of employees on IT experts’ positions or "just" business users of certain IT technologies for which the level of awareness of existing cybersecurity risks is extremely important. This research results define the current cybersecurity awareness level of students at Zagreb University of Applied Sciences (TVZ), what can be useful not only for teaching staff to form a curriculum related to cybersecurity more accurately but also to employers to know what to expect from their future employees regarding cybersecurity awareness level.

Keywords: student population cybersecurity awareness, cybersecurity awareness, cybersecurity, applied sciences students

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Authors: Yalçın Bozkurt

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Prediction models were developed for accurate prediction of bodyweight (BW) by using Digital Images of beef cattle body dimensions by Artificial Neural Networks (ANN). For this purpose, the animal data were collected at a private slaughter house and the digital images and the weights of each live animal were taken just before they were slaughtered and the body dimensions such as digital wither height (DJWH), digital body length (DJBL), digital body depth (DJBD), digital hip width (DJHW), digital hip height (DJHH) and digital pin bone length (DJPL) were determined from the images, using the data with 1069 observations for each traits. Then, prediction models were developed by ANN. Digital body measurements were analysed by ANN for body prediction and R2 values of DJBL, DJWH, DJHW, DJBD, DJHH and DJPL were approximately 94.32, 91.31, 80.70, 83.61, 89.45 and 70.56 % respectively. It can be concluded that in management situations where BW cannot be measured it can be predicted accurately by measuring DJBL and DJWH alone or both DJBD and even DJHH and different models may be needed to predict BW in different feeding and environmental conditions and breeds

Keywords: artificial neural networks, bodyweight, cattle, digital body measurements

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Authors: Lennon Furtado, Sandro Oliveira

Abstract:

The importance of an effective measurement program lies in the ability to control and predict what can be measured. Thus, the measurement program has the capacity to provide bases in decision-making to support the interests of an organization. Therefore, it is only possible to apply for an effective measurement program with a team of software engineers well trained in the measurement area. However, the literature indicates that are few computer science courses that have in their program the teaching of the software measurement process. And even these, generally present only basic theoretical concepts of said process and little or no measurement in practice, which results in the student's lack of motivation to learn the measurement process. In this context, according to some experts in software process improvements, one of the most used approaches to maintaining the motivation and commitment to software process improvements program is the use of the gamification. Therefore, this paper aims to present a proposal of teaching the measurement process by gamification. Which seeks to improve student motivation and performance in the assimilation of tasks related to software measurement, by incorporating elements of games into the practice of measurement process, making it more attractive for learning. And as a way of validating the proposal will be made a comparison between two distinct groups of 20 students of Software Quality class, a control group, and an experiment group. The control group will be the students that will not make use of the gamification proposal to learn software measurement process, while the experiment group, will be the students that will make use of the gamification proposal to learn software measurement process. Thus, this paper will analyze the objective and subjective results of each group. And as objective result will be analyzed the student grade reached at the end of the course, and as subjective results will be analyzed a post-course questionnaire with the opinion of each student about the teaching method. Finally, this paper aims to prove or refute the following hypothesis: If the gamification proposal to teach software measurement process does appropriate motivate the student, in order to attribute the necessary competence to the practical application of the measurement process.

Keywords: education, gamification, software measurement process, software engineering

Procedia PDF Downloads 317

Authors: Yuan Liu, Hongyun Liu

Abstract:

Teacher-student relationship plays an important role on facilitating students’ learning behavior, school engagement, and academic outcomes. It is believed that good relationship will enhance the human agency—the intrinsic motivation—mainly through the strengthening of autonomic support, feeling of relatedness, and the individual’s competence to increase the academic outcomes. This is in line with self-determination theory (SDT), which generally views that the intrinsic motivation imbedded with human basic needs is one of the most important factors that would lead to better school engagement, academic outcomes, and well-being. Based on SDT, the present study explored the relation of among teacher-student relationship (teacher’s encouragement, respect), students’ motivation (extrinsic and intrinsic), and achievement outcomes. The study was based on a large scale academic assessment and questionnaire survey conducted by the Center for Assessment and Improvement of Basic Education Quality in Mainland China (2013) on Grade 8 students. The results indicated that intrinsic motivation mediated the relation between teacher-student relationship and academic achievement outcomes.

Keywords: teacher-student relationship, intrinsic motivation, academic achievement, mediation

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Authors: Naman Solanki, Souraj Mondal

Abstract:

With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant’s engagement levels in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves the development of robust models that can capture facial micro-emotions efficiently. For the development of an engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and has a gold standard annotation for engagement prediction. Earlier research done using the DAiSEE dataset involved training and testing standard models like CNN-based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using the DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195, and 0.866 for predicting boredom level, engagement level, confusion level, and frustration level, respectively.

Keywords: computer vision, engagement prediction, deep learning, multi-level classification

Procedia PDF Downloads 115