PEER ASSESSMENT AS ACTIVE LEARNING METHOD

The importance of feedback in the student learning process is well understood among educational researchers (theorists) and teachers (practitioners). Its positive effects on the students learning and achievements have extensively been discussed in the theoretical research and illustrated by many empirical studies. It is also recognized that student assessment of other students' work is a useful tool for activating student engagement in the learning process. This work presents the results of a pilot study investigating the attitudes toward peer-assessment of examination papers and exploring experiences of the peer-assessment process of students pursuing a bachelor degree in statistics. BACKGROUND Examinations are usually used at the end of a study period, for example a module or a program. It is generally accepted that assessment is an unavoidable component of the teaching process because of its influence on learning. Feedback that follows examination is summative and focuses on telling students how they have performed. Constructive feedback is a good motivation for students to revise their performance and current knowledge in order to improve their learning. In this work we focus on the course ''Degree project'' (Department of Statistics, Stockholm University) which comprises two modules: Module 1, Bachelor's essay (BE); and Module 2, Discussion, i.e. peer assessment of BE. The expected learning outcomes of the course are the following. Having successfully completed the course, students will be able with a high grade of independence: (i) to formulate, analyze and adequately solve a statistical problem; (ii) to document a scientific work in a report; (iii) to review statistical reports and scientific studies critically; (iv) to orally present and discuss statistical reports. During this course students acquire skills on planning, organizing and conducting a project in some statistical area. There are many facets of the course that are vital for any applied or theoretical project, for example, students will have to find ways to apply or devise statistical methods in a field that is unfamiliar to them (e.g. criminology, climate research). Students will also practice to find relevant literature and write a scientific report. At the final seminar students will orally discuss other students' BE, and defend their own work. The main part of the course is the writing of a BE on a topic in applied or theoretical statistics. The work on the BE starts at the beginning of the autumn semester and is to be completed at the end of the same semester. Today, two students write jointly their BE under supervision of a teacher. Students may suggest a topic for their BE, or the topic may be given by the supervisor. Another compulsory part of the course is peer-assessment of a BE. Having successfully completed Module 2 students will be able to critically assess and discuss statistical reports and scientific studies. Module 2 is graded as either pass or fail. To pass Module 2 a student should satisfactorily discuss some other student's BE. In peer assessment, a student should be able, in particular, to assess arguments for the choice of statistical methods used in BE and critically review the obtained results and conclusions. The guidelines for peer assessment are given to the students at the beginning of the course. Module 2 comprises a 25 min oral presentation at the final seminar during which the peers should communicate their constructive critique to the authors of the BE (and the audience), and written report with comments to be submitted to the examiner and the author(s) before the final seminar. Students work on their peer assessment during a week. Today, peer assessment is often a single-mission activity at the end of the Bachelor's programme, and its effect on student learning is impossible to evaluate. This is why in this study we will focus on investigation of students' attitude towards peer assessment and experiences of the peer-assessment process

Survival in Hostile Conditions: Pupylation and the Proteasome in Actinobacterial Stress Response Pathways

Bacteria employ a multitude of strategies to cope with the challenges they face in their natural surroundings, be it as pathogens, commensals or free-living species in rapidly changing environments like soil. Mycobacteria and other Actinobacteria acquired proteasomal genes and evolved a post-translational, ubiquitin-like modification pathway called pupylation to support their survival under rapidly changing conditions and under stress. The proteasomal 20S core particle (20S CP) interacts with ring-shaped activators like the hexameric ATPase Mpa that recruits pupylated substrates. The proteasomal subunits, Mpa and pupylation enzymes are encoded in the so-called Pup-proteasome system (PPS) gene locus. Genes in this locus become vital for bacteria to survive during periods of stress. In the successful human pathogen Mycobacterium tuberculosis, the 20S CP is essential for survival in host macrophages. Other members of the PPS and proteasomal interactors are crucial for cellular homeostasis, for example during the DNA damage response, iron and copper regulation, and heat shock. The multiple pathways that the proteasome is involved in during different stress responses suggest that the PPS plays a vital role in bacterial protein quality control and adaptation to diverse challenging environments

On properties of Toeplitz-type covariance matrices in models with nested random effects

Models that capture symmetries present in the data have been widely used in different applications, with early examples from psychometric and medical research. The aim of this article is to study a random effects model focusing on the covariance structure that is block circular symmetric. Useful results are obtained for the spectra of these structured matrices

A new method for obtaining explicit estimators in unbalanced mixed linear models

The general unbalanced mixed linear model with two variance components is considered. Through resampling it is demonstrated how the fixed effects can be estimated explicitly. It is shown that the obtained nonlinear estimator is unbiased and its variance is also derived. A condition is given when the proposed estimator is recommended instead of the ordinary least squares estimator

Hierarchical Models with Block Circular Covariance Structures Hierarchical Models with Block Circular Covariance Structures: Estimation

Abstract Hierarchical linear models with a block circular covariance structure are considered. Sufficient conditions for obtaining explicit and unique estimators for the variance-covariance components are derived. Different restricted models are discussed and maximum likelihood estimators are presented.

Bilinear regression with random effects and reduced rank restrictions

Bilinear models with three types of effects are considered: fixed effects, random effects and latent variable effects. Explicit estimators are proposed

Bilinear regression with random effects and reduced rank restrictions

Bilinear models with three types of effects are considered: fixed effects, random effects and latent variable effects. Explicit estimators are proposed

Small area estimation using reduced rank regression models

Small area estimation techniques have got a lot of attention during the last decades due to their important applications in survey studies. Mixed linear models and reduced rank regression analysis are jointly used when considering small area estimation. Estimates of parameters are presented as well as prediction of random effects and unobserved area measurements.Funding Agencies|Swedish Foundation for Humanities and Social Sciences [P14-0641:1]; Swedish Natural Research Council [2017-03003]</p

Bilinear regression with random effects and reduced rank restrictions

Bilinear models with three types of effects are considered: fixed effects, random effects and latent variable effects. In the literature, bilinear models with random effects and bilinear models with latent variables have been discussed but there are no results available when combining random effects and latent variables. It is shown, via appropriate vector space decompositions, how to remove the random effects so that a well-known model comprising only fixed effects and latent variables is obtained. The spaces are chosen so that the likelihood function can be factored in a convenient and interpretable way. To obtain explicit estimators, an important standardization constraint on the random effects is assumed to hold. A theorem is presented where a complete solution to the estimation problem is given