PROMOTING INCLUSION IN ONLINE FIRST-YEAR CHEMISTRY THROUGH THE IMPLEMENTATION OF THE UNIVERSAL DESIGN FOR LEARNING FRAMEWORK

BACKGROUND The Universal Design for Learning (UDL) framework promotes inclusion by minimising barriers against, and maximising opportunities for learning. Implementing the three principles of the UDL framework (providing multiple means of representation, action and expression, and engagement) through its 31 checkpoints, provides strategies that allow diverse learners optimal participation in a meaningful and challenging learning environment. AIMS This paper will present an exploratory multiple-case design implementing UDL in first-year chemistry courses at two universities in Australia and one in the Philippines. DESIGN AND METHODS The UDL framework was integrated in the design and delivery of five chemistry topics, namely, periodic table and trends, chemical bonding, Lewis structures, molecular shapes, and polarity. Survey, focus groups, and interviews were conducted to gather students’ perceptions on the impact of UDL-based features in their learning. RESULTS Results from surveys, focus groups, and interviews reveal that, irrespective of their individual contexts, students from these three universities perceived positive impacts from the UDL-based features of their online chemistry learning environment. Students reported that their learning benefitted from provisions for enhanced visualisation of chemistry concepts, especially those that require chemical representations (i.e. bond formation, chemical structures, molecular geometry), improved accuracy, flexibility, self-evaluation of progress, and increased motivation. CONCLUSIONS These results suggest that applying the UDL framework in a first-year chemistry online environment can support and further enhance students’ learning irrespective of their individual contexts

EVALUATING LEARNING DESIGN OF FIRST-YEAR CHEMISTRY THROUGH LEARNING ANALYTICS

BACKGROUND Learning analytics, which involves the measurement, collection analysis and reporting of data about learners and their contexts may provide understanding and optimisation of learning environments. Recently, there has been growing interest among various education sectors in utilising learners’ data from different sources to provide support for the achievement of their specific learning goals. The expansion of online learning has yielded a rise of big data which may be employed to guide educators in designing learning environments, that together with appropriate instructional materials and methods, are able to address challenges in bridging discipline content and pedagogy. AIMS This study explored the use of learning analytics to evaluate the learning design developed for selected topics in first-year chemistry: namely periodic table, Lewis structures, types of chemical bonds, molecular shape and polarity. DESIGN AND METHODS After two weeks of online delivery of these topics to 985 learners, the log data from Moodle were collected, de-identified, processed and analysed. The aim of the analysis was to gain an understanding of learners’ interaction with the resources and activities posted on the LMS, and their online engagement with their peers and teachers. RESULTS Results from learning analytics measurements suggest that the prepared learning design afforded students not only flexible, but also independent learning, as evidenced by the usage pattern of Moodle activities over a 24-hour time frame. The log data recorded a greater frequency of access to interactive resources i.e. simulation (1721 times) and hypertext (1903 times) than the narrative resources i.e. videos (1526 times), web-based book (1561 times). This result suggests that learners choose the type of resources they perceived were most beneficial for their learning. In addition to learning resources, learners were likewise given the opportunity to select their preferred formative self-assessment activities. Results showed that more students accessed the worksheets rather than the timed quizzes. CONCLUSIONS Based on the analysis of learners’ data on their interaction with the learning resources and engagement in learning activities in the LMS, various information may be obtained to evaluate the learning design of an online first-year chemistry program

Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras

Live sheep export has become a public concern. This study aimed to test a non-contact biometric system based on artificial intelligence to assess heat stress of sheep to be potentially used as automated animal welfare assessment in farms and while in transport. Skin temperature (°C) from head features were extracted from infrared thermal videos (IRTV) using automated tracking algorithms. Two parameter engineering procedures from RGB videos were performed to assess Heart Rate (HR) in beats per minute (BPM) and respiration rate (RR) in breaths per minute (BrPM): (i) using changes in luminosity of the green (G) channel and (ii) changes in the green to red (a) from the CIELAB color scale. A supervised machine learning (ML) classification model was developed using raw RR parameters as inputs to classify cutoff frequencies for low, medium, and high respiration rate (Model 1). A supervised ML regression model was developed using raw HR and RR parameters from Model 1 (Model 2). Results showed that Models 1 and 2 were highly accurate in the estimation of RR frequency level with 96% overall accuracy (Model 1), and HR and RR with R = 0.94 and slope = 0.76 (Model 2) without statistical signs of overfitting

ICT + RBL + TPCK + UDL + OER = Innovative instructional design for blended undergraduate chemistry courses

The rapid advancement in information and computer technology (ICT) has contributed to massive development of new pedagogies for the enhancement of teaching and learning across all fields of education, at all levels, including tertiary chemistry education. For the most part, the worldwide web (WWW) has afforded chemistry educators vast opportunities to improve their teaching practices towards better student learning experience. From a static traditional text-based content delivery, chemistry educators nowadays may take advantage of a dynamic, learner-centered, multimodal instruction that caters to 21st century learners. A wide range of learning resources for chemistry education in various media formats have been made available through the internet and can be harnessed not only for content delivery but for assessment as well. To maximise the benefits of these affordances, an appropriate instructional design is however imperative. This paper discusses various educational models such as resource-based learning (RBL), technological pedagogical content knowledge (TPCK) and universal design for learning (UDL) which may serve as foundations for an innovative instructional design for use in the delivery of an undergraduate chemistry course/unit in blended learning mode. The use of web-based open educational resources (OERs) in the field of chemistry will likewise be discussed in this paper

Queerness in the digital age: a scholarly roundtable

The Velvet Light Trap gathered a diverse group of scholars with a range of specialties related to queer theory and media. This round-table touches on everything from dating apps to the films of John Waters to a livestreamed Indigo Girls concert, demonstrating the myriad ways digitality has affected queer media, representation, and audiences. The researchers began this discussion on 9 March 2020, only for closures due to the COVID-19 pandemic to begin in earnest a few days later. Thus, the participants' contributions began to reflect this fraught period toward the end of the conversation.Cities, Migration and Global Interdependenc

Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence

New and emerging technologies, especially those based on non-invasive video and thermal infrared cameras, can be readily tested on robotic milking facilities. In this research, implemented non-invasive computer vision methods to estimate cow’s heart rate, respiration rate, and abrupt movements captured using RGB cameras and machine learning modelling to predict eye temperature, milk production and quality are presented. RGB and infrared thermal videos (IRTV) were acquired from cows using a robotic milking facility. Results from 102 different cows with replicates (n = 150) showed that an artificial neural network (ANN) model using only inputs from RGB cameras presented high accuracy (R = 0.96) in predicting eye temperature (°C), using IRTV as ground truth, daily milk productivity (kg-milk-day−1), cow milk productivity (kg-milk-cow−1), milk fat (%) and milk protein (%) with no signs of overfitting. The ANN model developed was deployed using an independent 132 cow samples obtained on different days, which also rendered high accuracy and was similar to the model development (R = 0.93). This model can be easily applied using affordable RGB camera systems to obtain all the proposed targets, including eye temperature, which can also be used to model animal welfare and biotic/abiotic stress. Furthermore, these models can be readily deployed in conventional dairy farms

Implementing blended first-year chemistry in a developing country using online resources

Decades of rapid development in information and communication technologies (ICTs) have resulted in tremendous global evolution in computer and online instruction. Many developing countries, however, are still struggling to successfully integrate ICTs in their teaching and learning practices, subsequently leading to slower rates of adapting digital learning pedagogies. To understand how blended instruction might operate in higher education in a developing country, this study explored students’ perspectives on the implementation of blended learning in a first-year chemistry program delivered in the Philippines. Through the resource-based learning framework, multiple types of online learning resources were employed for blended delivery of topics on periodic trends, chemical bonding, Lewis structures, molecular shape and polarity through the learning management system, Moodle. To understand the students’ experiences, a mixed methods approach was employed through a survey, focus groups, and learning analytics. Despite the scarcity of technological resources (such as access to a reliable internet connection) 57.5% of 447 student respondents favoured blended learning because of the flexibility, wider access to different types of interactive learning resources, variety of learning activities, and perceived increase in learning productivity. While the majority of respondents (75.7%) had ICT skills sufficient for education, much fewer had access to computers (19.7%). 40.0% of students self-reported that they preferred a traditional mode of instruction primarily due to the perceived difficulty of chemistry as subject matter and the students’ perceived need for a physically-present teacher to explain concepts face-to-face and to respond to questions that would arise anytime during the learning period

Animal biometric assessment using non-invasive computer vision and machine learning are good predictors of dairy cows age and welfare: The future of automated veterinary support systems

Digitally extracted biometrics from visible videos of farm animals could be used to automatically assess animal welfare, contributing to the future of automated veterinary support systems. This study proposed using non-invasive video acquisition and biometric analysis of dairy cows in a robotic dairy farm (RDF) located at the Dookie campus, The University of Melbourne, Australia. Data extracted from dairy cows were used to develop two machine learning models: a biometrics regression model (Model 1) targeting (i) somatic cell count, (ii) weight, (iii) rumination, and (iv) feed intake and a classification model (Model 2) mapping features from dairy cow's face to predict animal age. Results showed that Model 1 achieved a high correlation coefficient (R = 0.96), slope (b = 0.96), and performance, and Model 2 had high accuracy (98%), low error (2%), and high performance without signs of under or overfitting. Models developed in this study can be used in parallel with other models to assess milk productivity, quality traits, and welfare for RDF and conventional dairy farms

Livestock Identification Using Deep Learning for Traceability

Farm livestock identification and welfare assessment using non-invasive digital technology have gained interest in agriculture in the last decade, especially for accurate traceability. This study aimed to develop a face recognition system for dairy farm cows using advanced deep-learning models and computer vision techniques. This approach is non-invasive and potentially applicable to other farm animals of importance for identification and welfare assessment. The video analysis pipeline follows standard human face recognition systems made of four significant steps: (i) face detection, (ii) face cropping, (iii) face encoding, and (iv) face lookup. Three deep learning (DL) models were used within the analysis pipeline: (i) face detector, (ii) landmark predictor, and (iii) face encoder. All DL models were finetuned through transfer learning on a dairy cow dataset collected from a robotic dairy farm located in the Dookie campus at The University of Melbourne, Australia. Results showed that the accuracy across videos from 89 different dairy cows achieved an overall accuracy of 84%. The computer program developed may be deployed on edge devices, and it was tested on NVIDIA Jetson Nano board with a camera stream. Furthermore, it could be integrated into welfare assessment previously developed by our research group

Non-invasive tools to detect smoke contamination in grapevine canopies, berries and wine: a remote sensing and machine learning modeling approach

Bushfires are becoming more frequent and intensive due to changing climate. Those that occur close to vineyards can cause smoke contamination of grapevines and grapes, which can affect wines, producing smoke-taint. At present, there are no available practical in-field tools available for detection of smoke contamination or taint in berries. This research proposes a non-invasive/in-field detection system for smoke contamination in grapevine canopies based on predictable changes in stomatal conductance patterns based on infrared thermal image analysis and machine learning modeling based on pattern recognition. A second model was also proposed to quantify levels of smoke-taint related compounds as targets in berries and wines using near-infrared spectroscopy (NIR) as inputs for machine learning fitting modeling. Results showed that the pattern recognition model to detect smoke contamination from canopies had 96% accuracy. The second model to predict smoke taint compounds in berries and wine fit the NIR data with a correlation coefficient (R) of 0.97 and with no indication of overfitting. These methods can offer grape growers quick, affordable, accurate, non-destructive in-field screening tools to assist in vineyard management practices to minimize smoke taint in wines with in-field applications using smartphones and unmanned aerial systems (UAS).Sigfredo Fuentes, Eden Jane Tongson, Roberta De Bei, Claudia Gonzalez Viejo, Renata Ristic, Stephen Tyerman, and Kerry Wilkinso