Hubungan Iklim Organisasi Sekolah dan Kompetensi Kecerdasan Emosi Guru Sebagai Mediator ke atas Kualiti Guru Generasi "Y" di Sekolah Menengah Daerah Kudat

Kajian ini dilakukan bertujuan untuk mengkaji persepsi guru generasi ‘Y’ mengenai iklim organisasi sekolah, kompetensi kecerdasaan emosi guru, dan kualiti guru generasi ‘Y’ dalam kalangan guru sekolah menengah di daerah Kudat, Sabah. Kajian bukan eksperimental ini menggunakan kaedah tinjauan dan beberapa teknik persampelan kebarangkalian telah digabungkan untuk mendapatkan sampel. Data dikumpul menggunakan satu set borang soal selidik adaptasi yang ditadbir ke atas 236 orang guru generasi ‘Y’ di sekolah menengah harian biasa. data dianalisis berdasarkan statistik deskriptif dan inferensi menggunakan perisian IBM SPSS dan AMOS-SEM Statistics Version 24. Dapatan kajian menunjukkan iklim organisasi, kompetensi kecerdasan emosi guru, dan kualiti guru generasi ‘Y’ diamalkan pada tahap tinggi. Seterusnya, tidak terdapat perbezaan yang signifikan secara statistik bagi iklim organisasi sekolah dan kompetensi kecerdasaan emosi guru, manakala kualiti guru generasi ‘Y’ terdapat perbezaan yang signifikan berdasarkan faktor jantina. Kajian ini juga, menunjukkan analisis laluan SEM membuktikan bahawa terdapat hubungan dan pengaruh secara langsung dan tidak langsung yang signifikan antara iklim organisasi dan kompetensi kecerdasan emosi guru terhadap kualiti guru generasi ‘Y’. Implikasi dan cadangan kajian lanjut turut dibincangkan

Experiments with Infinite-Horizon, Policy-Gradient Estimation

In this paper, we present algorithms that perform gradient ascent of the average reward in a partially observable Markov decision process (POMDP). These algorithms are based on GPOMDP, an algorithm introduced in a companion paper (Baxter and Bartlett, this volume), which computes biased estimates of the performance gradient in POMDPs. The algorithm's chief advantages are that it uses only one free parameter beta, which has a natural interpretation in terms of bias-variance trade-off, it requires no knowledge of the underlying state, and it can be applied to infinite state, control and observation spaces. We show how the gradient estimates produced by GPOMDP can be used to perform gradient ascent, both with a traditional stochastic-gradient algorithm, and with an algorithm based on conjugate-gradients that utilizes gradient information to bracket maxima in line searches. Experimental results are presented illustrating both the theoretical results of (Baxter and Bartlett, this volume) on a toy problem, and practical aspects of the algorithms on a number of more realistic problems

A reinforcement learning approach to rare trajectory sampling

Very often when studying non-equilibrium systems one is interested in analysing dynamical behaviour that occurs with very low probability, so called rare events. In practice, since rare events are by definition atypical, they are often difficult to access in a statistically significant way. What are required are strategies to "make rare events typical" so that they can be generated on demand. Here we present such a general approach to adaptively construct a dynamics that efficiently samples atypical events. We do so by exploiting the methods of reinforcement learning (RL), which refers to the set of machine learning techniques aimed at finding the optimal behaviour to maximise a reward associated with the dynamics. We consider the general perspective of dynamical trajectory ensembles, whereby rare events are described in terms of ensemble reweighting. By minimising the distance between a reweighted ensemble and that of a suitably parametrised controlled dynamics we arrive at a set of methods similar to those of RL to numerically approximate the optimal dynamics that realises the rare behaviour of interest. As simple illustrations we consider in detail the problem of excursions of a random walker, for the case of rare events with a finite time horizon; and the problem of a studying current statistics of a particle hopping in a ring geometry, for the case of an infinite time horizon. We discuss natural extensions of the ideas presented here, including to continuous-time Markov systems, first passage time problems and non-Markovian dynamics