Background The Moodle Learning Management System (LMS) is widely used in online teaching and learning, especially in STEM education. However, educational research on using Moodle is scattered throughout the literature. Therefore, this review aims to summarise this research to assist three sets of stakeholders—educators, researchers, and software developers. It identifies: (a) how and where Moodle has been adopted; (b) what the concerns, trends, and gaps are to lead future research and software development; and (c) innovative and effective methods for improving online teaching and learning. The review used the 4-step PRISMA-P process to identify 155 suitable journal articles from 104 journals in 55 countries published from January 2015 to June 2021. The database search was conducted with Scopus and Web of Science. Insights into the educational use of Moodle were determined through bibliometric analysis with Vosviewer outputs and thematic analysis. Results This review shows that Moodle is mainly used within University STEM disciplines and effectively improves student performance, satisfaction, and engagement. Moodle is increasingly being used as a platform for adaptive and collaborative learning and used to improve online assessments. The use of Moodle is developing rapidly to address academic integrity, ethics, and security issues to enhance speed and navigation, and incorporate artificial intelligence. Conclusion More qualitative research is required on the use of Moodle, particularly investigating educators’ perspectives. Further research is also needed on the use of Moodle in non-STEM and non-tertiary disciplines. Further studies need to incorporate educational theories when designing courses using the Moodle platform.

Background: Computer-aided learning management systems (LMSs) are widely used in higher education and are viewed as beneficial when transitioning from conventional face-to-face teaching to fully online courses. While LMSs have unique tools for transferring and assessing knowledge, their ability to engage and assess learners needs further investigation. This paper focuses on a study examining the LMS "Moodle" to ascertain the effectiveness of "Moodle quizzes" to improve, assess and distinguish knowledge in a civil engineering course at an Australian university. The course has a database comprising 62 formative and 61 summative quiz questions with embedded text, images, audio and video. This study investigates the use of these quiz questions with four course cohorts and 169 students. The quizzes assessed competencies of students during various stages of a study period through automated marking. The suitability of questions to assess and distinguish student knowledge levels was determined using a psychometric analysis based on facility index (FI) and the discrimination index (DI) statistics embedded within the Moodle quizzes. Results: This study highlights strategies used to set and review quiz questions for formative and summative assessments. Results indicated that students were engaged and satisfied in the formative assessment because they viewed the interactive videos between 2 and 6 times and 65% of students attempted all the formative questions. The FI indicated student pass rate for the summative questions and DI indicated the difficulty of these questions, while the combination of FI and DI results separated students with different knowledge levels.Using these Moodle statistics provided information to make effective decisions on how to improve the summative quizzes. Conclusion: The multimodal quizzes were effective in teaching and assessing a theoretical engineering course and provided efficient methods to replace conventional assessments. The FI and DI indexes are useful statistical tools in redesigning appropriate sets of questions. Time-poor academics will benefit from using these easily attainable Moodle statistics to inform decisions while revising the quizzes and making assessments more autonomous.

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a b s t r a c t Study region:The study is based on unregulated catchments located in Mt. Lofty, Northern and Yorke regions of South Australia (SA). Study focus: Hydrological losses, which are frequently used in design flood estimation, have a wide range of spatial and temporal variability. However, the current practice for many design applications is to use a single loss value. Adopting a single representative value for loss is likely to introduce a high degree of uncertainty and potential bias. This paper identifies the relationships between losses and other parameters that can be incorporated in hydrological models to make reasonably accurate estimates of the losses. This paper assesses the variability of losses and identifies a method that can model initial loss (IL) using the parameters total rainfall (TR), rainfall duration (D) and antecedent wetness (AW). This study is based on 1162 rainfall events from the selected catchments. New hydrological insights for the region: This paper introduces two nomographs, TR-D and TR-AW, which are implemented using k-colour maps and a central tendency method. The developed methods are then validated using the rainfall runoff model, Water Bound Network Model (WBNM). This study will yield improvements to existing loss models by utilising rainfall and antecedent data, instead of using representative values to generalise real situations.

Abstract. Accurate estimation of hydrological losses is required for making vital decisions in design applications that are based on design rainfall models and rainfall-runoff models. The use of representative single values of hydrological losses, despite their wide variability, is common practice, especially in Australian studies. This practice leads to issues such as over or under estimation of design floods. The probability distribution method is potentially a better technique to describe losses. However, a lack of understanding of how losses are distributed can limit the use of this technique. This paper aims to identify a probability distribution function that can successfully describe hydrological losses of a catchment of interest. The paper explains the systematic process of identifying probability distribution functions, the problems faced during the distribution fitting process and a new generalised method to test the adequacy of fitted distributions. The goodness-of-fit of the fitted distributions are examined using the Anderson-Darling test and the Q-Q plot method and the errors associated with quantile estimation are quantified by estimating the bias and mean square error (MSE). A two-parameter gamma distribution was identified as one that successfully describes initial loss (IL) data for the selected catchments. Further, non-parametric standardised distributions that describe both IL and continuing loss data are also identified. This paper will provide a significant contribution to the Australian Rainfall and Runoff (ARR) guidelines that are currently being updated, by improving understanding of hydrological losses in South Australian catchments. More importantly, this study provides new knowledge on how IL in a catchment is characterised.