http://hdl.handle.net/10453/41320 2026-04-09T16:14:24Z http://hdl.handle.net/10453/176793 Title: A casework study: The effect of the porcine digestive process on animal carcasses and human teeth Authors: Atwood, L; Lain, R; Kotzander, J; McCardle, P; Mason, B; Raymond, J; Sears, A Abstract: What happens if a human body is fed to pigs Although a popular notion in the entertainment industry no scientific published literature exists that documents this porcine feeding behaviour nor more importantly what elements of the cadaver may survive such a process A study conducted in 2020 born out of a casework enquiry aimed to investigate the following two questions Would pigs feed on a human body And if so what could be recovered post feeding event Kangaroo carcasses porcine carcasses as human analogues and 90 human teeth were prepared and fed to two domestic pigs in a variety of feed scenarios Biological traces including bones bone fragments teeth and tooth fragments were recovered both post digestion from the faeces of the pigs as well as uneaten from the porcine enclosure 29 of all human teeth were recovered from the study 35 of which were recovered post digestion from the faeces and 65 were recovered uneaten from the porcine enclosure Of the recovered human teeth 81 were deemed suitable for identification by a forensic odontologist From the 447 bones recovered from the enclosure 94 could be identified to a bone type and species From all 3338 bone fragments recovered from the faeces of the pigs none retained any morphological traits that would allow further intelligence to be generated Overall it was found that pigs will feed on human analogues and will consume soft tissue bones and human teeth Biological traces in the form of bones bone fragments teeth and tooth fragments may be recovered both post digestion from the faeces or from the porcine enclosure The biological traces can be used for identification of an individual via forensic odontology identification of a species via forensic anthropology and may be suitable for DNA analysis The outcomes of this study generated new avenues for investigation in the case and may be used to inform future operational resources 2023-04-01T00:00:00Z http://hdl.handle.net/10453/176727 Title: Using causal models to bridge the divide between big data and educational theory Authors: Kitto, K; Hicks, B; Buckingham Shum, S Abstract: An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research. Practitioner notes What is already known about this topic ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems. Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts. Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences. What this paper adds An overview of causal modelling to support educational data scientists interested in adopting this promising approach. A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories. An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent. Implications for practice and/or policy Causal models can help us to explicitly specify educational theories in a testable format. It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model. Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems. 2023-09-01T00:00:00Z http://hdl.handle.net/10453/176726 Title: Towards more replicable content analysis for learning analytics Authors: Kitto, K; Manly, CA; Ferguson, R; Poquet, O Abstract: Content analysis (CA) is a method frequently used in the learning sciences and so increasingly applied in learning analytics (LA). Despite this ubiquity, CA is a subtle method, with many complexities and decision points affecting the outcomes it generates. Although appearing to be a neutral quantitative approach, coding CA constructs requires an attention to decision making and context that aligns it with a more subjective, qualitative interpretation of data. Despite these challenges, we increasingly see the labels in CA-derived datasets used as training sets for machine learning (ML) methods in LA. However, the scarcity of widely shareable datasets means research groups usually work independently to generate labelled data, with few attempts made to compare practice and results across groups. A risk is emerging that different groups are coding constructs in different ways, leading to results that will not prove replicable. We report on two replication studies using a previously reported construct. A failure to achieve high inter-rater reliability suggests that coding of this scheme is not currently replicable across different research groups. We point to potential dangers in this result for those who would use ML to automate the detection of various educationally relevant constructs in LA. 2023-03-13T00:00:00Z http://hdl.handle.net/10453/176725 Title: Learner-centred Analytics of Feedback Content in Higher Education Authors: Lin, J; Dai, W; Lim, LA; Tsai, YS; Mello, RF; Khosravi, H; Gasevic, D; Chen, G Abstract: Feedback is an effective way to assist students in achieving learning goals. The conceptualisation of feedback is gradually moving from feedback as information to feedback as a learner-centred process. To demonstrate feedback effectiveness, feedback as a learner-centred process should be designed to provide quality feedback content and promote student learning outcomes on the subsequent task. However, it remains unclear how instructors adopt the learner-centred feedback framework for feedback provision in the teaching practice. Thus, our study made use of a comprehensive learner-centred feedback framework to analyse feedback content and identify the characteristics of feedback content among student groups with different performance changes. Specifically, we collected the instructors' feedback on two consecutive assignments offered by an introductory to data science course at the postgraduate level. On the basis of the first assignment, we used the status of student grade changes (i.e., students whose performance increased and those whose performance did not increase on the second assignment) as the proxy of the student learning outcomes. Then, we engineered and extracted features from the feedback content on the first assignment using a learner-centred feedback framework and further examined the differences of these features between different groups of student learning outcomes. Lastly, we used the features to predict student learning outcomes by using widely-used machine learning models and provided the interpretation of predicted results by using the SHapley Additive exPlanations (SHAP) framework. We found that 1) most features from the feedback content presented significant differences between the groups of student learning outcomes, 2) the gradient boost tree model could effectively predict student learning outcomes, and 3) SHAP could transparently interpret the feature importance on predictions. 2023-03-13T00:00:00Z