The Potential for Student Performance Prediction in Small Cohorts with Minimal Available Attributes

The measurement of student performance during their progress through university study provides academic leadership with critical information on each student’s likelihood of success. Academics have traditionally used their interactions with individual students through class activities and interim assessments to identify those “at risk” of failure/withdrawal. However, modern university environments, offering easy on-line availability of course material, may see reduced lecture/tutorial attendance, making such identification more challenging. Modern data mining and machine learning techniques provide increasingly accurate predictions of student examination assessment marks, although these approaches have focussed upon large student populations and wide ranges of data attributes per student. However, many university modules comprise relatively small student cohorts, with institutional protocols limiting the student attributes available for analysis. It appears that very little research attention has been devoted to this area of analysis and prediction. We describe an experiment conducted on a final-year university module student cohort of 23, where individual student data are limited to lecture/tutorial attendance, virtual learning environment accesses and intermediate assessments. We found potential for predicting individual student interim and final assessment marks in small student cohorts with very limited attributes and that these predictions could be useful to support module leaders in identifying students potentially “at risk.”.Peer reviewe

Kinetic growth walks on complex networks

Kinetically grown self-avoiding walks on various types of generalized random networks have been studied. Networks with short- and long-tailed degree distributions $P(k)$ were considered ($k$, degree or connectivity), including scale-free networks with $P(k) \sim k^{-\gamma}$. The long-range behaviour of self-avoiding walks on random networks is found to be determined by finite-size effects. The mean self-intersection length of non-reversal random walks, , scales as a power of the system size $N$: $ \sim N^{\beta}$, with an exponent $\beta = 0.5$ for short-tailed degree distributions and $\beta < 0.5$ for scale-free networks with $\gamma < 3$. The mean attrition length of kinetic growth walks, , scales as $\sim N^{\alpha}$, with an exponent $\alpha$ which depends on the lowest degree in the network. Results of approximate probabilistic calculations are supported by those derived from simulations of various kinds of networks. The efficiency of kinetic growth walks to explore networks is largely reduced by inhomogeneity in the degree distribution, as happens for scale-free networks.Comment: 10 pages, 8 figure

Random walk on the range of random walk

We study the random walk X on the range of a simple random walk on ℤ d in dimensions d≥4. When d≥5 we establish quenched and annealed scaling limits for the process X, which show that the intersections of the original simple random walk path are essentially unimportant. For d=4 our results are less precise, but we are able to show that any scaling limit for X will require logarithmic corrections to the polynomial scaling factors seen in higher dimensions. Furthermore, we demonstrate that when d=4 similar logarithmic corrections are necessary in describing the asymptotic behavior of the return probability of X to the origin

The IT way of loafing in class: Extending the theory of planned behavior (TPB) to understand students’ cyberslacking intentions

YesInternet-enabled technologies can facilitate students' learning, engagement, and productivity but they also present challenges by way of distraction. Cyberslacking is the use of internet-enabled technologies by students in class for non-class related activities. This research attempts to understand the factors that influence students' cyberslacking intentions in class, through extending the Theory of Planned Behavior with lack of attention, apathy towards course material, distraction by others, perceived threat, and escapism. Quantitative data were collected (n = 188) using a survey method with undergraduate and postgraduate students from a management school in a British university. All eight proposed hypotheses were found to be supported. The findings indicated that constructs such as lack of attention, apathy towards course material, and distraction by others are significant predictors of attitude. Further, attitude, subjective norm, perceived behavioral control, perceived threat, and escapism were found to significantly influence students’ cyberslacking intentions

The effect of characteristics of source credibility on consumer behaviour: a meta-analysis

YesThe aim of this research is to synthesise findings from existing studies on the characteristics of source credibility of electronic word of mouth (eWOM) communications in a single model by using meta-analysis. Findings from 20 research papers show that source expertise, trustworthiness, and homophily significantly influence perceived eWOM usefulness and credibility, intention to purchase, and information adoption. The results of this study add to existing knowledge of the influence of source characteristics on consumer behaviour, which will advance our understanding of information processing. Marketers can use the findings of this meta-analysis to enhance their marketing activities

Scaling of Self-Avoiding Walks in High Dimensions

We examine self-avoiding walks in dimensions 4 to 8 using high-precision Monte-Carlo simulations up to length N=16384, providing the first such results in dimensions $d > 4$ on which we concentrate our analysis. We analyse the scaling behaviour of the partition function and the statistics of nearest-neighbour contacts, as well as the average geometric size of the walks, and compare our results to $1/d$-expansions and to excellent rigorous bounds that exist. In particular, we obtain precise values for the connective constants, $\mu_5=8.838544(3)$, $\mu_6=10.878094(4)$, $\mu_7=12.902817(3)$, $\mu_8=14.919257(2)$ and give a revised estimate of $\mu_4=6.774043(5)$. All of these are by at least one order of magnitude more accurate than those previously given (from other approaches in $d>4$ and all approaches in $d=4$). Our results are consistent with most theoretical predictions, though in $d=5$ we find clear evidence of anomalous $N^{-1/2}$-corrections for the scaling of the geometric size of the walks, which we understand as a non-analytic correction to scaling of the general form $N^{(4-d)/2}$ (not present in pure Gaussian random walks).Comment: 14 pages, 2 figure

Mental health morbidity among people subject to immigration detention in the UK: a feasibility study

Aims: The UK has one of the largest systems of immigration detention in Europe.. Those detained include asylum-seekers and foreign national prisoners, groups with a higher prevalence of mental health vulnerabilities compared with the general population. In light of little published research on the mental health status of detainees in immigration removal centres (IRCs), the primary aim of this study was to explore whether it was feasible to conduct psychiatric research in such a setting. A secondary aim was to compare the mental health of those seeking asylum with the rest of the detainees. Methods: Cross-sectional study with simple random sampling followed by opportunistic sampling. Exclusion criteria included inadequate knowledge of English and European Union nationality. Six validated tools were used to screen for mental health disorders including developmental disorders like Personality Disorder, Attention Deficit Hyperactivity Disorder (ADHD), Autistic Spectrum Disorder (ASD) and Intellectual Disability, as well as for needs assessment. These were the MINI v6, SAPAS, AQ-10, ASRS, LDSQ and CANFOR. Demographic data were obtained using a participant demographic sheet. Researchers were trained in the use of the screening battery and inter-rater reliability assessed by joint ratings. Results: A total of 101 subjects were interviewed. Overall response rate was 39%. The most prevalent screened mental disorder was depression (52.5%), followed by personality disorder (34.7%) and post-traumatic stress disorder (20.8%). 21.8% were at moderate to high suicidal risk. 14.9 and 13.9% screened positive for ASD and ADHD, respectively. The greatest unmet needs were in the areas of intimate relationships (76.2%), psychological distress (72.3%) and sexual expression (71.3%). Overall presence of mental disorder was comparable with levels found in prisons. The numbers in each group were too small to carry out any further analysis. Conclusion: It is feasible to undertake a psychiatric morbidity survey in an IRC. Limitations of the study include potential selection bias, use of screening tools, use of single-site study, high refusal rates, the lack of interpreters and lack of women and children in study sample. Future studies should involve the in-reach team to recruit participants and should be run by a steering group consisting of clinicians from the IRC as well as academics

The scaling limit of the incipient infinite cluster in high-dimensional percolation. II. Integrated super-Brownian excursion

For independent nearest-neighbour bond percolation on Z^d with d >> 6, we prove that the incipient infinite cluster's two-point function and three-point function converge to those of integrated super-Brownian excursion (ISE) in the scaling limit. The proof is based on an extension of the new expansion for percolation derived in a previous paper, and involves treating the magnetic field as a complex variable. A special case of our result for the two-point function implies that the probability that the cluster of the origin consists of n sites, at the critical point, is given by a multiple of n^{-3/2}, plus an error term of order n^{-3/2-\epsilon} with \epsilon >0. This is a strong statement that the critical exponent delta is given by delta =2.Comment: 56 pages, 3 Postscript figures, in AMS-LaTeX, with graphicx, epic, and xr package