Improving Statistical Education through a Learning Design

This paper presents the results of a study examining the student learning experience of statistics within e-learning environment at the University of Wollongong. This study involves a cohort of 191 undergraduate students who enrolled in an Introductory Statistics subject in Autumn 2010 session. A learning design map was used within the subject e-learning site aiming at providing guidance which details out timing tasks and resources, and supports materials on week-by-week basis to students in learning the subject. The findings reveal the students gained benefits from the use of the map in helping them to learn and understand the subject; however they highlight some issues on the design of subject particularly within e-learning environment in terms of browser compatibility, file accessibility, map layout, and choices of design varieties. The paper concludes with a discussion on the needs of learning design in teaching practices and the learning of statistics and followed by suggestions for further research

Harmful Gases Profiling in Meru Menora Tunnel using SICK Sensor

This paper discusses the study on the measured harmful gases due to traffic emission in the Meru Menora Tunnel, a Malaysia highway tunnel. The hazardous gasses data would help in promoting essential ventilation system inside the tunnel for the health and safety of the users. The emission gasses concentration reading is divided into two main components comprise of Nitrogen Dioxide (NO2), and Carbon Monoxide (CO). Other than that, the visibility also been measured by using SICK sensor. The measurement has been done during normal, festive and school holiday seasons. Festive season shows the highest number of traffic and thus giving the worst air quality. Ventilation fan system can be activated based on the concentration level of gases and visibility in the tunnel

Assessing students’ abilities in interpreting the correlation and regression analysis

A case study was carried out on students who were being exposed to some theoretical concepts of the correlation and regression topics to investigate their ability to compute and interpret the Pearson’s correlation coefficient and the slope of regression. The findings revealed that a low percentage of students (19.43%) successfully completed their interpretation of correlation coefficient and 33.18% of the students managed to interpret the computed value of regression slope completely. It was also found that the students’ ability to interpret regression slope was significantly associated with the ability to interpret the correlation coefficient correctly. It is hoped that the findings obtained from this study will shed some light on improving teaching practices of statistics educators so as to help students in gaining better understanding on interpreting the correlation and regression analysis.Keywords: correlation; Pearson; regression; coefficients; interpretation; students’ abilit

Measuring students’ understanding in counting rules and its probability via e-learning mode: a Rasch measurement approach

Probability is a study of the rules that offers the foundational theory for the development of statistics. This sets out the investigation where students’ understanding of counting rules and its probability were explored using the Rasch measurement approach. A test instrument with 20 items was developed and administered to 74 students taking the STA150 Probability and Statistics course. Data were captured through an interactive e-learning platform that is dmodo.com and analyzed using Winsteps 3.81.0. The results from the Wright map showed that 83.8% of the students have the ability that matched well with the difficulty of the while 16.2% of the students need to be given more attention on the topic. The study was also able to show that the items can be replicated in other samples of similar characteristics. Keywords: students’ understanding; counting rules; probability Raschmeasurement model; Wright map

Harmful gases profiling in Meru Menora Tunnel using SICK sensor

This paper discusses the study on the measured harmful gases due to traffic emission in the Meru Menora Tunnel, a Malaysia highway tunnel. The hazardous gasses data would help in promoting essential ventilation system inside the tunnel for the health and safety of the users. The emission gasses concentration reading is divided into two main components comprise of Nitrogen Dioxide (NO2), and Carbon Monoxide (CO). Other than that, the visibility also been measured by using SICK sensor. The measurement has been done during normal, festive and school holiday seasons. Festive season shows the highest number of traffic and thus giving the worst air quality. Ventilation fan system can be activated based on the concentration level of gases and visibility in the tunnel

The neural correlates of emotion in decision-making

Both neuroscientific and psychology methods are used to study and explain the active neurons of the individuals' brain response when exposed to external stimuli. This study analyses the relevance literature and investigates the neural correlates of emotion, rewards, and motivation in the decision-making process, the emotional interactions between children, adolescents, and ageing. It was reviewed the literature to explore if neuroscientific methods provide accurate information about the role of emotion, reward, and motivation in decision- making mechanisms. The findings showed that the amygdala, medial prefrontal cortex, and ventromedial prefrontal cortex play a central role in processing of emotion which in turn influence decision-making process. While individuals with lesion in the ventromedial prefrontal cortex which is responsible for emotional responses toward risk, reward, and decision-making are not good decision-makers. In addition, the prefrontal cortex plays central role in approach and withdrawal motivational, whereby the right prefrontal cortex associated with withdrawal behavior and the left prefrontal cortex associated with approach behavior

An intelligent content prefix classification approach for quality of service optimization in information-centric networking

This research proposes an intelligent classification framework for quality of service (QoS) performance improvement in information-centric networking (ICN). The proposal works towards keyword classification techniques to obtain the most valuable information via suitable content prefixes in ICN. In this study, we have achieved the intelligent function using Artificial Intelligence (AI) implementation. Particularly, to find the most suitable and promising intelligent approach for maintaining QoS matrices, we have evaluated various AI algorithms, including evolutionary algorithms (EA), swarm intelligence (SI), and machine learning (ML) by using the cost function to assess their classification performances. With the goal of enabling a complete ICN prefix classification solution, we also propose a hybrid implementation to optimize classification performances by integration of relevant AI algorithms. This hybrid mechanism searches for a final minimum structure to prevent the local optima from happening. By simulation, the evaluation results show that the proposal outperforms EA and ML in terms of network resource utilization and response delay for QoS performance optimization

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC