Grassroots Moodle

Over the past two decades or so we have witnessed the transition of the web from novelty to ubiquity. The web has become pervasive in our work environments, a necessity we can no longer do without. The emergence of Web 2.0 technologies brought “read/write” access to the web for the masses and thus caused (r)evolutions in fields relying on economics of scarcity. When we started using Moodle in 2006, many Web 2.0 technologies were already mature, but the use of Moodle was just beginning to gain ground in the field of education, which is generally cautious when experimenting with change. The aim of this paper is to collect and assess the data and experiences that have been developed over the six years during which Moodle has been in use at our Faculty. A case study methodology was chosen for qualitative investigation into some of the technical and organizational implications of implementing Moodle from a bottom-up approach. As of this year, the Faculty's Moodle is being used by 55% of our teaching staff and 82% of our students and is managed by one administrator

DETERMINING SUBJECT-SPECIFIC PARAMETERS FOR A COMPUTER SIMULATION MODEL OF A ONE-HANDED TENNIS BACKHAND

A subject-specific computer simulation model of a one-handed tennis backhand was developed to investigate the mechanisms that may cause injury to the elbow. Subject-specific parameters for the ball, racket and human were determined. Firing tennis balls from a pneumatic air cannon onto a force plate enabled parameters to be determined for a spring-damper model of a tennis ball. Data from further ball cannon tests allowed the spring constants for the stringbed and the coefficient of friction between the ball and stringbed to be optimised using a computer simulation model. The fundamental modal frequencies of the racket frame were obtained by Doppler laser vibrometry and its inertia parameters were determined from the results of oscillation and balance tests. An elite tennis player performed isovelocity tests at the wrist, elbow and shoulder to establish torque / angle / angular velocity relationships. Inertia parameters of the human segments were calculated from ninety-five anthropometric measurements using a geometric model

Alleviating Linear Ecological Bias and Optimal Design with Subsample Data

In this paper, we illustrate that combining ecological data with subsample data in situations in which a linear model is appropriate provides three main benefits. First, by including the individual level subsample data, the biases associated with linear ecological inference can be eliminated. Second, by supplementing the subsample data with ecological data, the information about parameters will be increased. Third, we can use readily available ecological data to design optimal subsampling schemes, so as to further increase the information about parameters. We present an application of this methodology to the classic problem of estimating the effect of a college degree on wages. We show that combining ecological data with subsample data provides precise estimates of this value, and that optimal subsampling schemes (conditional on the ecological data) can provide good precision with only a fraction of the observations

Using educational analytics to improve test performance

Learning analytics are being used in many educational applications in order to help students and Faculty. In our work we use predictive analytics, using student behaviour to predict the likely performance of end of semester final grades with a system we call PredictED. The main contribution of our approach is that our intervention automatically emailed students on a regular basis, with our prediction for the outcome of their exam performance. We targeted first year, first semester University students who often struggle with making the transition into University life where they are given much more responsibility for things like attending class, completing assignments, etc. The form of student behaviour that we used is students’ levels and types of engagement with the University’s Virtual Learning Environment (VLE), Moodle. We mined the Moodle access log files for a range of parameters based on temporal as well as content access, and use machine learning techniques to predict likely pass/fail, on a weekly basis throughout the semester using logs and outcomes from previous years as training material. We chose ten first-year modules with reasonably high failure rates, large enrolments and stability of module content across the years to implement an early warning system on. From these modules 1,558 students were registered for one of these modules. They were offered the chance to opt into receiving weekly email alerts warning them about their likely outcome. Of these 75% or 1,181 students opted into this service. Pre-intervention there were no differences between participants and non-participants on a number of measures related to previous academic record. However, post- intervention the first-attempt final grade performance yielded nearly 3% improvement (58.4% to 61.2%) on average for those who opted in. This tells us that providing weekly guidance and personalised feedback to vulnerable first year students, automatically generated from monitoring of their online behaviour, has a significant positive effect on their exam performance

Examining the relationships between attendance, online engagement and summative examinations performance

Background: Non-attendance correlates with poor performance, but manual recording of attendance is problematic. Online activity reports may be a more efficient method of identifying at-risk students. Summary of work: This research is part of a prospective study examining physical attendance, online activity reports (Moodle), continuous assessments and summative examination performance. Ethical approval was granted by RCSI Ethics Committee. Two modules within the first year of the undergraduate medical program were identified for inclusion. Results: Data from 2 RCSI modules are presented (NM and AS). A single cohort of 365 students undertook both modules, 30 of whom were repeating. Comparison of medians showed significant reductions in all parameters within the repeat student group. In NM, regression analysis showed continuous assessment had the largest effect size on summative examinations for both first-time and repeat student groups (R2 = 0.545; R2 = 0.289). Among repeat students, online access of lecture notes had a larger effect size than physical attendance at small group tutorials, while both these indices were less contributory (R2 \u3c 0.1) for first-time students. In AS, continuous assessment showed the largest effect size for first-time students (R2 = 0.585), while online access of lecture notes was most contributory among repeat students (R2 = 0.35). Conclusions: Effect sizes are most notable for continuous assessment, but online activity correlates with summative performance and is more predictive for outcomes among repeat students than physical attendance. These indices may be useful to screen at-risk students for individual intervention and support

NUMERICAL INVESTIGATION OF HEEL-SHOE INTERACTION IN RUNNING

Human heel sensitivity to mechanical loading, which is associated with the strain/stress state around the sensory receptors, is an important body function for sport, exercise and other daily activities (Lake & lafortune, 1998; Patritti, 2002). The sensory receptors within the heel transmit the mechanical signals (e.g. strains) into neural signals and enable human body to sense and adapt to changes of external loadings. To improve the understanding of the mechanics of this process, a realistic numerical model was developed in this work to establish quantitative relationships between the external loads and the state of stresses/strains at sensory receptor locations in running

Subject-specific computer simulation model for determining elbow loading in one-handed tennis backhand groundstrokes.

A subject-specific angle-driven computer model of a tennis player, combined with a forward dynamics, equipment-specific computer model of tennis ball–racket impacts, was developed to determine the effect of ball–racket impacts on loading at the elbow for one-handed backhand groundstrokes. Matching subject-specific computer simulations of a typical topspin/slice one-handed backhand groundstroke performed by an elite tennis player were done with root mean square differences between performance and matching simulations of < 0.5°over a 50 ms period starting from ball impact. Simulation results suggest that for similar ball–racket impact conditions, the difference in elbow loading for a topspin and slice one-handed backhand groundstroke is relatively small. In this study, the relatively small differences in elbow loading may be due to comparable angle–time histories at the wrist and elbow joints with the major kinematic differences occurring at the shoulder. Using a subject-specific angle-driven computer model combined with a forward dynamics, equipment-specific computer model of tennis ball–racket impacts allows peak internal loading, net impulse, and shock due to ball–racket impact to be calculated which would not otherwise be possible without impractical invasive techniques. This study provides a basis for further investigation of the factors that may increase elbow loading during tennis strokes

Student data: data is knowledge – putting the knowledge back in the students’ hands

Learning Management Systems are integral technologies within higher education institutions. These tools automatically amass large amounts of log data relating to student activities. The field of learning analytics uses data from learning management systems (LMSs) and student information systems to track student progress and predict future performance in order to enhance learning environments (Siemens, 2011). The aim of this paper is to describe a project where we utilized a system developed in Dublin City University to use information about student engagement with our LMS, Moodle, to create a model predicting pass or failure in certain modules. The project is divided into three distinct phases. An initial investigation was completed analyzing Moodle activity for the last six years. The purpose of this exercise was to determine automatically if “trends” could be identified linking Moodle engagement with student attainment. This was done by training a machine learning classifier to map student online behaviour, against outcomes. Once the classifier was trained, several modules were identified as suitable for building a predictor of student exam success.Ten modules were identified for semester 1 with a further seven identified for semester 2. The second phase involved analyzing current students’ engagement with these modules and sending students information about the predictions of their attainment for the module, based on their Moodle engagement. At this stage concerns were raised within the university that the data that we share with the students could actually have the opposite effect to what we are after, i.e. the student may look at the data and think that there is no point in putting in more effort as ‘I’m too far behind already’. Dietz-Uhler and Hurn refer to this as “instead of being a constructive tool, feedback becomes a prophet of failure” (Dietz-Uhler, 2013). This contention was addressed by conducting an online survey with students in an effort to explore their experiences of being provided with feedback regarding their engagement with the LMS. The third and final phase of this project was the development of a dashboard for lecturers to enable monitoring of their students’ engagement with their module on Moodle. This enables lecturers to have an overview of how students are engaging with their course on Moodle and quickly identify students who are not engaging with the LMS and who are potentially at risk of failure or non-completion. There are numerous examples of the use of learning analytics in higher education. This study focuses on the provision of data obtained through learning analytics to the student and qualitative analysis that was conducted in relation to this data. This research adds to the existing research into learning analytics being used for student retention

Suddenly moving large classes online: Illuminating the experience of the teaching staff in one university

[EN] In early 2020, the transition of large classes from the face-to-face to the online context occurred overnight and at scale at a time when the crisis was being faced at all levels of society, nationally and internationally. This paper is based on research which examined the impact of this sudden transition on large classes in Dublin City University with a view to illuminating the experience to inform future practice (Authors., in press). A rapid, systemised review of literature was carried out with the aim of contextualising data gathered through surveys with staff and students in relation to our experience of moving large classes online in the early stages of the Covid-19 pandemic. While the study examined the impact from the perspectives of teaching staff and students, this paper reports on the perspectives of teaching staff only. Large class teachers found this experience challenging, reporting a sense of isolation and worry. However it would seem that opportunity was seen in the face of adversity, whereby staff have identified potential for better ways of doing things going forward as a result of their experiences between March and May 2020.Glynn, M.; Farrell, AM.; Buckley, K.; Lowney, R.; Smyth, S.; Stone, S. (2021). Suddenly moving large classes online: Illuminating the experience of the teaching staff in one university. En 7th International Conference on Higher Education Advances (HEAd'21). Editorial Universitat Politècnica de València. 171-178. https://doi.org/10.4995/HEAd21.2021.13032OCS17117

A systematic review of selected interventions to reduce juvenile re-offending. Technical Report.

What do we want to know? Persistent juvenile re-offending remains an area of concern for public policy, due to the social, economic and health impacts of such offending on victims and offenders. A large proportion of criminal offences are committed by repeat offenders. The broad purpose of this systematic review was to review the research evidence on a selected range of interventions to reduce re-offending by juveniles to try and identify more effective interventions. What did we find and what are the implications? When compared to standard diversion (caution and monitoring) there was consistent evidence of reductions in re-offending from the following intervention: Pre-sentencing diversion with personal skills training and reparation The intervention included: - personal skills training/ counselling about anger management, personal responsibility and decision making. - some form of reparation to the community/ victim of crime. - family involvement. When compared to standard residential placement there was consistent evidence of reductions in re-offending from the following intervention: • Community based family residential placement for female juvenile offenders The intervention included: - residential placement for six months to a year in small group supportive ‘family type’ environment. - personal skills training/counselling which is about anger management, personal responsibility and decision making. - monitoring and use of appropriate incentives and sanctions. Promising effects The following interventions were classified as having promising positive effects with limited or inconsistent evidence: • ‘Teen Courts’ compared to other diversion • Community based family residential placements compared to standard residential placements for male juvenile offenders Insufficient evidence There was insufficient evidence identified to assess the impact of the following interventions: • Secure incarceration compared to community sentence • Psycho-dynamic counselling compared to normal court interventions • Pre-sentence diversions compared to court community sentence • Multi-component diversion for persistent offenders (comparison not clear) • Multi-component diversion for mixed groups of offence severity (comparison not clear) • Supported transition from secure incarceration to community compared to no or limited support • Probation plus sports counselling compared to probation only • Violence re-education programme compared to court imposed community service What are the implications? The results suggest that those interventions where there is consistent evidence of beneficial effect could be priorities for possible implementation accompanied by rigorous evaluation in the UK context as the evidence on the effects of this intervention in this review all came from the USA. The ‘promising’ interventions could be considered priorities for further rigorous evaluation. How did we get these results? The review was undertaken in a number of stages. The first stage consisted of identifying all studies that met the review inclusion criteria published between 1998 and 2007. Descriptive information about these studies was collected and used as a ‘map’ of research in the field of interventions to reduce juvenile re-offending. At this point there were 94 studies included the map. A further round of coding was undertaken to help identify sub-groups of studies. The results of this coding were discussed with the steering group and a decision was made at that point to focus on a number of subgroups for the in-depth review. At this stage detailed data extraction was undertaken to assess the quality of the studies and facilitate synthesis of the findings of the selected studies in order to provide answers to the review questions