Keep an "eye" on the text: The Use of Self-mention in the Academic Writing of Experts

One of the most controversial inquiries in academic writing is whether it is admissible to use first person pronouns in a scientific paper or not. Many professors discourage their students from using them, rather favoring a more passive tone, and thus causing novices to avoid inserting themselves into their texts in an expert-like manner. Abundant research, however, has recently attested that negotiation of identity is plausible in academic prose, and there is no need for a paper to be void of an authorial identity. Because in the course of the English Studies Degree we have received opposing prompts in the use of I, the aim of this dissertation is to throw some light upon this vexed issue. To this end, I compiled a corpus of 16 Research Articles (RAs) that comprises two sub-corpora, one featuring Linguistics RAs and the other one Literature RAs, and each, in turn, consists of articles written by American and British authors. I then searched for real occurrences of I, me, my, mine, we, us, our and ours, and studied their frequency, rhetorical functions and distribution along each paper. The results obtained certainly show that academic writing is no longer the faceless prose that it used to be, for I is highly used in both disciplines and varieties of English. Concerning functions, the most typically used roles were the use of I to take credit for the writer’s research process, and also those involving plural forms. With respect to the spatial disposition, all sections welcomed first person pronouns, but the Method and the Results/Discussion sections seem to stimulate their appearance. On the basis of these findings, I suggest that an L2 writing pedagogy that is mindful not only of the language proficiency, but also of the students’ own identity may have a beneficial effect on the composition of their texts.

Convergence Results for the EM Approach to Mixtures of Experts Architectures

The Expectation-Maximization (EM) algorithm is an iterative approach to maximum likelihood parameter estimation. Jordan and Jacobs (1993) recently proposed an EM algorithm for the mixture of experts architecture of Jacobs, Jordan, Nowlan and Hinton (1991) and the hierarchical mixture of experts architecture of Jordan and Jacobs (1992). They showed empirically that the EM algorithm for these architectures yields significantly faster convergence than gradient ascent. In the current paper we provide a theoretical analysis of this algorithm. We show that the algorithm can be regarded as a variable metric algorithm with its searching direction having a positive projection on the gradient of the log likelihood. We also analyze the convergence of the algorithm and provide an explicit expression for the convergence rate. In addition, we describe an acceleration technique that yields a significant speedup in simulation experiments.

Learning Fine Motion by Markov Mixtures of Experts

Compliant control is a standard method for performing fine manipulation tasks, like grasping and assembly, but it requires estimation of the state of contact between the robot arm and the objects involved. Here we present a method to learn a model of the movement from measured data. The method requires little or no prior knowledge and the resulting model explicitly estimates the state of contact. The current state of contact is viewed as the hidden state variable of a discrete HMM. The control dependent transition probabilities between states are modeled as parametrized functions of the measurement We show that their parameters can be estimated from measurements concurrently with the estimation of the parameters of the movement in each state of contact. The learning algorithm is a variant of the EM procedure. The E step is computed exactly; solving the M step exactly would require solving a set of coupled nonlinear algebraic equations in the parameters. Instead, gradient ascent is used to produce an increase in likelihood.

Reasoning Modeled as a Society of Communicating Experts

This report describes a domain independent reasoning system. The system uses a frame-based knowledge representation language and various reasoning techniques including constraint propagation, progressive refinement, natural deduction and explicit control of reasoning. A computational architecture based on active objects which operate by exchanging messages is developed and it is shown how this architecture supports reasoning activity. The user interacts with the system by specifying frames and by giving descriptions defining the problem situation. The system uses its reasoning capacity to build up a model of the problem situation from which a solution can interactively be extracted. Examples are discussed from a variety of domains, including electronic circuits, mechanical devices and music. The main thesis is that a reasoning system is best viewed as a parallel system whose control and data are distributed over a large network of processors that interact by exchanging messages. Such a system will be metaphorically described as a society of communicating experts.

Espaces d’interdépendance entre action publique et experts: Etat réflexif dans le champ du sida

info:eu-repo/semantics/published

Using EMGs and kinematics data to study the take-off technique of experts and novices for a pole vaulting short run-up educational exercise

This study attempts to characterise the electromyographic activity and kinematics exhibited during the performance of take-off for a pole vaulting short run-up educational exercise, for different expertise levels. Two groups (experts and novices) participated in this study. Both groups were asked to execute their take-off technique for that specific exercise. Among the kinematics variables studied, the knee, hip and ankle angles and the hip and knee angular velocities were significantly different. There were also significant differences in the EMG variables, especially in terms of (i) biceps femoris and gastrocnemius lateralis activity at touchdown and (ii) vastus lateralis and gastrocnemius lateralis activity during take-off. During touchdown, the experts tended to increase the stiffness of the take-off leg to decrease braking. Novices exhibited less stiffness in the take-off leg due to their tendency to maintain a tighter knee angle. Novices also transferred less energy forward during take-off due to lack of contraction in the vastus lateralis, which is known to contribute to forward energy transfers. This study highlights the differences in both groups in terms of muscular and angular control according to the studied variables. Such studies of pole vaulting could be useful to help novices to learn expert's technique.

Experts on e-learning: insights gained from listening to the student voice!

The Student Experience of e-Learning Laboratory (SEEL) project at the University of Greenwich was designed to explore and then implement a number of approaches to investigate learners’ experiences of using technology to support their learning. In this paper members of the SEEL team present initial findings from a University-wide survey of nearly a 1000 students. A selection of 90 ‘cameos’, drawn from the survey data, offer further insights into personal perceptions of e-learning and illustrate the diversity of students experiences. The cameos provide a more coherent picture of individual student experience based on the totality of each person’s responses to the questionnaire. Finally, extracts from follow-up case studies, based on interviews with a small number of students, allow us to ‘hear’ the student voice more clearly. Issues arising from an analysis of the data include student preferences for communication and social networking tools, views on the ‘smartness’ of their tutors’ uses of technology and perceptions of the value of e-learning. A primary finding and the focus of this paper, is that students effectively arrive at their own individualised selection, configuration and use of technologies and software that meets their perceived needs. This ‘personalisation’ does not imply that such configurations are the most efficient, nor does it automatically suggest that effective learning is occurring. SEEL reminds us that learners are individuals, who approach learning both with and without technology in their own distinctive ways. Hearing, understanding and responding to the student voice is fundamental in maximising learning effectiveness. Institutions should consider actively developing the capacity of academic staff to advise students on the usefulness of particular online tools and resources in support of learning and consider the potential benefits of building on what students already use in their everyday lives. Given the widespread perception that students tend to be ‘digital natives’ and academic staff ‘digital immigrants’ (Prensky, 2001), this could represent a considerable cultural challenge.