Continuing professional development needs and activities of school psychologists

The study investigated school psychologists' continuing professional development (CPD) needs and their relationship to selected demographic factors, preservice training, incentives, and typical engagement in various CPD activities. A survey questionnaire was mailed to 500 randomly selected National Association of School Psychologists (NASP) members, and responses were received from 235. Respondents reported their greatest CPD needs to be in the areas of direct service and consultation. No demographic variables were related to respondents' CPD needs. Few preservice training or incentive factors were related to CPD needs. Respondents' perceived value of preservice training in CPD management was related to their CPD needs, but few respondents reported that they had received this type of preservice training. Several personal incentives for CPD, including self‐assessment of CPD needs, opportunity to practice new skills and receive feedback during CPD training, and opportunity to evaluate the effectiveness of CPD training and CPD goal attainment were related to CPD needs. Respondents' CPD needs in each specific skill area, including direct service, consultation, assessment, program planning and evaluation, supervision, and research, had significant but low correlations with the amount of actual CPD activity in the respective skill area. © 2001 John Wiley & Sons, Inc.     

Using ROI to demonstrate performance value in the public sector

The demand for accountability through measurement continues to increase. Although much progress has been made, it remains an issue that challenges even the most sophisticated and progressive performance improvement function. This article provides an overview of best practices in the return‐on‐investment process and describes how applying these measurement practices can have a positive impact on organizational performance and the credibility of performance improvement professionals in the public sector.     

The ruins of neo-liberalism and the construction of a new (scientific) subjectivity

Given my long-time interests in neoliberalism and questions of subjectivity, I am pleased to respond to Jesse Bazzul’s paper, “Neoliberal Ideology, global capitalism, and science education: Engaging the question of subjectivity.” In what follows, I first summarize what I see as Bazzul’s contributions to pushing science education in ‘post’ directions. I next introduce the concept of “post-neoliberalism” as a tool in this endeavor. Finally, I address what all of this might have to do with subjectivity in the context of science education. I speak as a much-involved veteran of a version of the science wars fought out in education research for the last decade (NRC 2002). My interest is to use this “battle” to think politics and science anew toward an engaged social science, without certainty, rethinking subjectivity, the unconscious and bodies where I ask “what kind of science for what kind of politics?”     

Teacher perceptions of an online tutoring program for elementary mathematics

This study explores elementary teacher perceptions related to the implementation of an online tutoring program. Teachers were surveyed regarding factors that affected use of the online tutoring program as a supplement to mathematics instruction. Results indicated that teachers overwhelmingly reported positive views of the training and support opportunities provided, as well as confidence in their level of knowledge and the ease of use of the program. Teachers also indicated strong support of the mathematical content and instructional value of the program. Each of these factors increases the likelihood that online tutoring programs such as the one implemented in this study will continue to be used among teachers as a supplement to mathematics instruction.     

Eliciting pupil perspectives in primary mathematics: possibilities and issues

This article considers various approaches to consulting primary pupils about mathematics. This is done first through a literature review and second by drawing on our experience of designing and piloting pupil consultation in collaboration with staff in one primary school. Our concern is with the utility and drawbacks of the methods used rather than with substantive issues. We conclude that useful information can be gained from both the planning process and outcomes, but caution must be exercised in interpreting pupil responses. Social processes are involved and the possibility of social desirability effects must be acknowledged. Finally, we indicate the usefulness of using different approaches together.     

A joint learning approach with knowledge injection for zero-shot cross-lingual hate speech detection

Hate speech is an increasingly important societal issue in the era of digital communication. Hateful expressions often make use of figurative language and, although they represent, in some sense, the dark side of language, they are also often prime examples of creative use of language. While hate speech is a global phenomenon, current studies on automatic hate speech detection are typically framed in a monolingual setting. In this work, we explore hate speech detection in low-resource languages by transferring knowledge from a resource-rich language, English, in a zero-shot learning fashion. We experiment with traditional and recent neural architectures, and propose two joint-learning models, using different multilingual language representations to transfer knowledge between pairs of languages. We also evaluate the impact of additional knowledge in our experiment, by incorporating information from a multilingual lexicon of abusive words. The results show that our joint-learning models achieve the best performance on most languages. However, a simple approach that uses machine translation and a pre-trained English language model achieves a robust performance. In contrast, Multilingual BERT fails to obtain a good performance in cross-lingual hate speech detection. We also experimentally found that the external knowledge from a multilingual abusive lexicon is able to improve the models’ performance, specifically in detecting the positive class. The results of our experimental evaluation highlight a number of challenges and issues in this particular task. One of the main challenges is related to the issue of current benchmarks for hate speech detection, in particular how bias related to the topical focus in the datasets influences the classification performance. The insufficient ability of current multilingual language models to transfer knowledge between languages in the specific hate speech detection task also remain an open problem. However, our experimental evaluation and our qualitative analysis show how the explicit integration of linguistic knowledge from a structured abusive language lexicon helps to alleviate this issue.