<i>‘What retention’ means to me</i>: the position of the adult learner in student retention

Studies of student retention and progression overwhelmingly appear adopt definitions that place the institution, rather than the student, at the centre. Retention is most often conceived in terms of linear and continuous progress between institutionally identified start and end points. This paper reports on research that considered data from 38 in-depth interviews conducted with individuals who had characteristics often associated with non-traditional engagement in higher education who between 2006 and 2010 had studied an ‘Introduction to HE’ module at one distance higher education institution, some of whom had progressed to further study at that institution, some of whom had not. The research deployed a life histories approach to seek a finer grained understanding of how individuals conceptualise their own learning journey and experience, in order to reflect on institutional conceptions of student retention. The findings highlight potential anomalies hidden within institutional retention rates – large proportions of the interview participants who were not ‘retained’ by the institution reported successful progression to and in other learning institutions and environments, both formal and informal. Nearly all described positive perspectives on lifelong learning which were either engendered or improved by the learning undertaken. This attests to the complexity of individuals’ lives and provides clear evidence that institution-centric definitions of retention and progression are insufficient to create truly meaningful understanding of successful individual learning journeys and experiences. It is argued that only through careful consideration of the lived experience of students and a re-conception of measures of retention, will we be able to offer real insight into improving student retention

A hazard model of the probability of medical school dropout in the United Kingdom

From individual level longitudinal data for two entire cohorts of medical students in UK universities, we use multilevel models to analyse the probability that an individual student will drop out of medical school. We find that academic preparedness—both in terms of previous subjects studied and levels of attainment therein—is the major influence on withdrawal by medical students. Additionally, males and more mature students are more likely to withdraw than females or younger students respectively. We find evidence that the factors influencing the decision to transfer course differ from those affecting the decision to drop out for other reasons

Investigating Student Communities with Network Analysis of Interactions in a Physics Learning Center

Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. Finn and Rock [1] link the academic and social integration of students to increased rates of retention. We utilize social network analysis to quantify interactions in Florida International University's Physics Learning Center (PLC) that support the development of academic and social integration,. The tools of social network analysis allow us to visualize and quantify student interactions, and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors which contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of supportive learning community.Comment: 14 pages, 3 tables, 4 figure

Algebraic approach to time-delay data analysis for LISA

Cancellation of laser frequency noise in interferometers is crucial for attaining the requisite sensitivity of the triangular 3-spacecraft LISA configuration. Raw laser noise is several orders of magnitude above the other noises and thus it is essential to bring it down to the level of other noises such as shot, acceleration, etc. Since it is impossible to maintain equal distances between spacecrafts, laser noise cancellation must be achieved by appropriately combining the six beams with appropriate time-delays. It has been shown in several recent papers that such combinations are possible. In this paper, we present a rigorous and systematic formalism based on algebraic geometrical methods involving computational commutative algebra, which generates in principle {\it all} the data combinations cancelling the laser frequency noise. The relevant data combinations form the first module of syzygies, as it is called in the literature of algebraic geometry. The module is over a polynomial ring in three variables, the three variables corresponding to the three time-delays around the LISA triangle. Specifically, we list several sets of generators for the module whose linear combinations with polynomial coefficients generate the entire module. We find that this formalism can also be extended in a straight forward way to cancel Doppler shifts due to optical bench motions. The two modules are infact isomorphic. We use our formalism to obtain the transfer functions for the six beams and for the generators. We specifically investigate monochromatic gravitational wave sources in the LISA band and carry out the maximisiation over linear combinations of the generators of the signal-to-noise ratios with the frequency and source direction angles as parameters.Comment: 27 Pages, 6 figure

Improving the Sensitivity of LISA

It has been shown in the past, that the six Doppler data streams obtained LISA configuration can be combined by appropriately delaying the data streams for cancelling the laser frequency noise. Raw laser noise is several orders of magnitude above the other noises and thus it is essential to bring it down to the level of shot, acceleration noises. A rigorous and systematic formalism using the techniques of computational commutative algebra was developed which generates all the data combinations cancelling the laser frequency noise. The relevant data combinations form a first module of syzygies. In this paper we use this formalism for optimisation of the LISA sensitivity by analysing the noise and signal covariance matrices. The signal covariance matrix, averaged over polarisations and directions, is calculated for binaries whose frequency changes at most adiabatically. We then present the extremal SNR curves for all the data combinations in the module. They correspond to the eigenvectors of the noise and signal covariance matrices. We construct LISA `network' SNR by combining the outputs of the eigenvectors which improves the LISA sensitivity substantially. The maximum SNR curve can yield an improvement upto 70 % over the Michelson, mainly at high frequencies, while the improvement using the network SNR ranges from 40 % to over 100 %. Finally, we describe a simple toy model, in which LISA rotates in a plane. In this analysis, we estimate the improvement in the LISA sensitivity, if one switches from one data combination to another as it rotates. Here the improvement in sensitivity, if one switches optimally over three cyclic data combinations of the eigenvector is about 55 % on an average over the LISA band-width. The corresponding SNR improvement is 60 %, if one maximises over the module.Comment: 16 pages, 10 figures, Submitted to Class. Quant. Gravit

Using Effect Size in Evaluating Academic Engagement and Motivation in a Private Business School

This research analyses student engagement and motivation data gathered from a UK-based private business university and multiple European public universities. The data was obtained using an Internet-based generic expert system called Evolute. In this research, the self-evaluation results from 40 undergraduate business school students were subjected to comparison analysis using an effect size described by Cohen’s d-values. Using the effect size in the analysis helps to easily identify the areas or the specific items where the benchmarked university is doing well compared to others, as well as to find out the areas or items that could be subjected for improvement. According to the results, the benchmarked institution scored higher mean values in 95% of statements than all the other cases conducted with the instrument at public universities

On searches for gravitational waves from mini creation event by laser interferometric detectors

As an alternative view to the standard big bang cosmology the quasi-steady state cosmology(QSSC) argues that the universe was not created in a single great explosion; it neither had a beginning nor will it ever come to an end. The creation of new matter in the universe is a regular feature occurring through finite explosive events. Each creation event is called a mini-bang or, a mini creation event(MCE). Gravitational waves are expected to be generated due to any anisotropy present in this process of creation. Mini creation event ejecting matter in two oppositely directed jets is thus a source of gravitational waves which can in principle be detected by laser interferometric detectors. In the present work we consider the gravitational waveforms propagated by linear jets and then estimate the response of laser interferometric detectors like LIGO and LISA

Class Attendance and Students’ Evaluations of Teaching: Do No-Shows Bias Course Ratings and Rankings?

Background: Many university departments use students’ evaluations of teaching (SET) to compare and rank courses. However, absenteeism from class is often nonrandom and, therefore, SET for different courses might not be comparable. Objective: The present study aims to answer two questions. Are SET positively biased due to absenteeism? Do procedures, which adjust for absenteeism, change course rankings? Research Design: The author discusses the problem from a missing data perspective and present empirical results from regression models to determine which factors are simultaneously associated with students’ class attendance and course ratings. In order to determine the extent of these biases, the author then corrects average ratings for students’ absenteeism and inspect changes in course rankings resulting from this adjustment. Subjects: The author analyzes SET data on the individual level. One or more course ratings are available for each student. Measures: Individual course ratings and absenteeism served as the key outcomes. Results: Absenteeism decreases with rising teaching quality. Furthermore, both factors are systematically related to student and course attributes. Weighting students’ ratings by actual absenteeism leads to mostly small changes in ranks, which follow a power law. Only a few, average courses are disproportionally influenced by the adjustment. Weighting by predicted absenteeism leads to very small changes in ranks. Again, average courses are more strongly affected than courses of very high or low in quality. Conclusions: No-shows bias course ratings and rankings. SET are more appropriate to identify high- and low-quality courses than to determine the exact ranks of average courses