Based on the Actiotope Model of Giftedness, this article introduces a learningresource-oriented approach for gifted education. It provides a comprehensive categorization of learning resources, including five exogenous learning resources termed educational capital and five endogenous learning resources termed learning capital. Evidence from the literature is presented that supports the validity of each of the categories. Finally, 12 implications of a learning resource orientation for gifted education are discussed in relation to four areas: gifted education orientations, gifted identification, gifted education principles, and gifted learning resources management.

This research examined the relationships among personality factors, social support, emotional wellbeing, and academic achievement in 65 gifted secondary students, a sample drawn from a longitudinal study of over 950 students. The research demonstrated that, compared to their nongifted peers, gifted students had significantly higher academic outcomes for all subject areas except Geography and Physical Education. Teachers rated the gifted students as being welladjusted and less likely to have behavioural or emotional problems than nongifted students. The gifted students, however, reported feeling more sad and less satisfied with their social support than their nongifted counterparts. There were no significant differences in terms of self-esteem, trait hope, problem orientation, or attitudes towards education. Within the gifted sample, the research found that the students who were most likely to get poor grades were those who scored high in psychoticism and low in conscientiousness, trait hope, joviality, and in attitudes towards schools. Interestingly, self-esteem was entirely unrelated to gifted performance.

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Lectures are a central element of traditional university learning, but Australian lecturers increasingly face very low levels of lecture attendance. A significant amount of research exists that investigates the drivers of lecture attendance. However, those studies typically study single factors in an isolated manner, thus overestimating the importance of individual factors. This study contributes to the understanding of lecture attendance (and nonattendance) by including a range of factors that potentially affect lecture attendance simultaneously, thus accounting for possible interactions between factors and identifying the key drivers of lecture attendance. The study uses a survey among all students of an Australian university to compute a regression model with the probability of lecture attendance as the dependent variable. Results indicate that only four of the factors previously investigated are significant for marketing students (i.e., the difficulty of the subject, the quality of the lecture as perceived by the student, the quality of the student as indicated by his or her average mark, and the format of the lecture), which leaves little opportunity for Australian universities to improve attendance with simple measures. Instead, the data suggest that universities need to improve the quality of lectures to achieve better attendance levels. Lectures are a central element of traditional university learning, but Australian lecturers increasingly face very low levels of lecture attendance (Rodgers, 2001). A significant amount of research exists that investigates the drivers of lecture attendance. However, those studies typically study single factors in an isolated manner, thus overestimating the importance of individual factors. The present study contributes to our understanding of lecture attendance (and non-attendance) by including a range of factors that potentially affect lecture attendance simultaneously, thus accounting for possible interactions between factors and identifying the key drivers of lecture attendance. We used a survey among all students of an Australian university to compute a regression model with the probability of lecture attendance as the dependent variable.Our results indicate that only four of the factors previously investigated are significant for marketing students (that is, the difficulty of the subject, the quality of the lecture as perceived by the student, the quality of the student as indicated by their average mark, and the format of the lecture), which leaves little opportunity for Australian universities to improve attendance with simple measures. Instead, the data suggests that universities need to improve the quality of lectures to achieve better attendance levels. 2