Exploratory analysis of marked point patterns has previously been conducted using two disjoint techniques, namely the mark correlation function and spectral analysis. Our purpose here is to present two alternative autocovariance estimators to the mark correlation function which not only apply in both planar and lattice situations, but which in the lattice case can also be considered in terms of the inverse Fourier transform of the spectrum. Moreover, they can be applied to isotropic or anisotropic marked point patterns. Various examples are presented to show how these estimators perform when applied to data sets possessing different kinds of mark structure, and a rank test procedure is proposed to enable the construction of empirical tests of hypothesis.

According to Albert Bandura (1977, 1992, 1994, 1995), self-efficacy is «the belief in one's capabilities to organize and execute the courses of action required to manage prospective situations». A person's attitudes, abilities and cognitive skills comprise what is known as the self-system. This system plays a major role in how we perceive situations and how we behave in response to different situations. Self-efficacy is essential component of this self-system. In other words, self-efficacy is a person's belief in his or her ability to succeed in a particular situation. In the international literature, self-efficacy is an important topic among psychologists and educators. As Bandura and other researchers have demonstrated, self-efficacy can have an impact on behavior and motivation. To assess teacher self-efficacy, we applied the Teacher Self-Efficacy Scale by Tschannen-Moran & Woolfolk Hoy, 2001: for the Italian translation and adaptation we interviewed 200 teachers of primary and secondary schools * Questo articolo è il risultato del lavoro congiunto dei quattro autori. In particolare G. Domenici ha scritto il par. 1; N. Patrizi il par. 2; V. Biasi il par. 3; R. Capobianco il par. 5; il par. 4 è frutto della stesura a più mani dei quattro autori.

This paper presents the procedures and the outcomes of collaborative activities and of peer tutoring carried out by the students in the teaching of "General Didactics", held at the University of Roma Tre, in ay 2012/13.

International studies shows that in the universitary teaching is becoming increasingly widespread use of digital environments that integrated traditional teaching. In this paper we consider the contribution that can be offered by online environments to develop in students a positive and proactive dimension to the academic curriculum, so as to reduce the drop-out, encourage greater regularity in the academic career, develop transversal competences and promote the orientation. In particular we observe the activities online in academic year 2013/14 done by the students of General Didactic, a course of study established at the Department of Educational Sciences of the University of Roma Tre. These activities include the realization of two online laboratory: completion of the QSA (Questionnaire for Learning Strategies); participation in a cooperative online learning practice. In both experiences we analyzed the procedures and outcomes

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Exploratory analysis of marked point patterns has previously been conducted using two disjoint techniques, namely the mark correlation function and spectral analysis. Our purpose here is to present two alternative autocovariance estimators to the mark correlation function which not only apply in both planar and lattice situations, but which in the lattice case can also be considered in terms of the inverse Fourier transform of the spectrum. Moreover, they can be applied to isotropic or anisotropic marked point patterns. Various examples are presented to show how these estimators perform when applied to data sets possessing different kinds of mark structure, and a rank test procedure is proposed to enable the construction of empirical tests of hypothesis.

A generalization of the Probit model is presented, with the extended skew-normal cumulative distribution as a link function, which can be used for modelling a binary response variable in the presence of selectivity bias. The estimate of the parameters via ML is addressed, and inference on the parameters expressing the degree of selection is discussed. The assumption underlying the model is that the selection mechanism influences the unmeasured factors and does not affect the explanatory variables. When this assumption is violated, but other conditional independencies hold, then the model proposed here is derived. In particular, the instrumental variable formula still applies and the model results at the second stage of the estimating procedure.

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