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In Australia, all teachers are expected to explicitly attend to numeracy in the subjects they teach. Pre-service teachers, therefore, need to begin to develop effective numeracy teaching strategies but there is a limited research base to inform the design of courses that address this need. This paper reports on findings from a study investigating the impact of one such course. The paper draws on data from two pre-service teachers: two course tasks completed at the beginning of the course and interviews conducted at the end of the course. Data were analysed using the 21st Century Numeracy Model. Findings suggest there were changes to the pre-service teachers' understanding of numeracy and their capacity to embed numeracy in the subjects they will teach.

Prospective secondary mathematics teachers in Australia are typically taught by mathematics educators and mathematicians who work in different faculties and seldom collaborate--a situation that can lead to conflicting views about how to teach mathematics. This paper reports on findings from semi-structured interviews with three mathematics teacher educators in a hybrid mathematics/mathematics education department. Valsiner's zone theory is used to analyse how their beliefs, institutional context and professional learning opportunities shape their identities as MTEs. Findings reveal that the MTEs had developed similar beliefs within supportive institutional context but drew on different sources of professional learning.

Secondary mathematics pre-service teachers often have different experiences of mathematics and its teaching and learning during their initial teacher education. This paper documents the beliefs about mathematics, its teaching, and its learning, of mathematicians and mathematics educators who teach secondary mathematics pre-service teachers. The beliefs of the surveyed sample of eighty-two academics and differences between groups were characterised using descriptive statistics and one-way comparisons between groups ANOVA. Generally, respondents had a Problem-solving view of mathematics and those with education backgrounds were more in agreement with that method of teaching.

The purpose of this study was to explore the factorial structure of motivation and perception items from a student survey utilised as part of the Reframing Mathematical Futures II (RMFII) Project. Data was collected in 2017 from 442 students in Years 7 to 10 from various different States across Australia. An exploratory factor analysis identified four factors which were consistent with the studies the items were adapted from: Intrinsic and Cognitive Value of Mathematics, Instrumental Value of Mathematics, Mathematics Effort, and Social Impact of School Mathematics. An analysis of variance (ANOVA) also revealed that there were statistically significant differences between Year Level and State for some of these factors.

To support teachers in their quest to incorporate reasoning as a mathematical proficiency as espoused in the Australian Curriculum: Mathematics, a professional learning research project using demonstration lessons was carried out. This paper reports on the impact of demonstration lessons on one participating teacher's pedagogical knowledge about reasoning. The growth in this teacher's knowledge was analysed using a phenomenographic framework established to evaluate teachers' development in mathematical reasoning. The results show that demonstration and subsequent trial lessons contributed to her growth.

Self-belief can directly predict students' academic motivation and achievement. Research indicates that mathematical self-belief often decreases during the middle years of schooling. This study explored the mathematical self-belief development of 15 Year 7 students. Data were gathered from a survey, a mathematics achievement test and interviews. Results were analysed and interpreted from a multilevel perspective. Findings indicate that student-level characteristics, such as persistence, were the most influential on mathematical self-belief. While class-level contexts, such as ability grouping, were less influential, interpersonal relationships with teachers played a major role. [The research reported in this paper was part of a larger project supported by an Australian Research Council Linkage grant.]

The use of high-stakes assessment to measure students' mathematical performance has become commonplace in schools all over the world. Such assessment instruments provide national or international comparisons of student (and potentially teacher performance). Each form of assessment is specialised in nature and is characteristic of the culture and intent of the governing bodies. The purpose of this paper is to highlight differences and similarities between two national high-stakes assessments and the possible implications to students' sense making.

"Make It Count" was a large scale, government-funded, project aimed at improving the mathematics learning of Indigenous students. NAPLAN Numeracy test results were used as one measure of the effect of the program. In this paper we report on the performance on these tests of Indigenous students in schools involved in the project. Group data and, where available, longitudinal data for individual students are reported.

Using an innovative recruitment tool, the social network site Facebook, survey data were gathered from samples of the Australian general public and from around the world. Views on the gendering of mathematics, science, and ICT were gathered. In this paper we report the findings from six of the 15 questions on the survey, and only from respondents in predominantly English-speaking countries. The findings reveal that the majority was not gender-stereotyped about mathematics and related careers. However, if a gendered view was held, it was overwhelming to endorse the male stereotype. Male respondents' views were more strongly gendered than were females'. [For the complete proceedings, see ED585874.]

Declining participation rates in advanced mathematics courses and STEM-related occupations has been an issue in Australia for some time, particularly for females. As students continue to disengage with mathematics and complain about its usefulness, it is important to explore what we can do to stem the tide of departing students. One area worthy of investigation is students' interest in mathematics including whether they are able to name a mathematical role model in their lives. Forty-three students in Years 7 to 9 from three schools were asked to name people they knew who were interested in mathematics. There was a strong bias towards male figures (44 to 17), particularly fathers and male peers.

The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a fast pace and affecting academia and professional practice in many ways. Paradigms such as just-in-time learning, constructivism, student-centered learning and collaborative approaches have emerged and are being supported by technological advancements such as simulations, virtual reality and multi-agents systems. These developments have created both opportunities and areas of serious concerns. This conference aimed to cover both technological as well as pedagogical issues related to these developments. The IADIS CELDA 2012 Conference received 98 submissions from more than 24 countries. Out of the papers submitted, 29 were accepted as full papers. In addition to the presentation of full papers, short papers and reflection papers, the conference also includes a keynote presentation from internationally distinguished researchers. Individual papers contain figures, tables, and references.

The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented educational software and databases of student test scores, has created large repositories of data reflecting how students learn. The EDM conference focuses on computational approaches for using those data to address important educational questions. The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus. This publication presents the following papers: (1) A Comparison of Student Skill Knowledge Estimates (Elizabeth Ayers, Rebecca Nugent, Nema Dean); (2) Differences Between Intelligent Tutor Lessons, and the Choice to Go Off-Task (Ryan S.J.d. Baker); (3) A User-Driven and Data-Driven Approach for Supporting Teachers in Reflection and Adaptation of Adaptive Tutorials (Dror Ben-Naim, Michael Bain, and Nadine Marcus); (4) Detecting Symptoms of Low Performance Using Production Rules (Javier Bravo and Alvaro Ortigosa); (5) Predicting Students Drop Out: A Case Study (Gerben W. Dekker, Mykola Pechenizkiy and Jan M. Vleeshouwers); (6) Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning (Mingyu Feng, Joseph E. Beck and Neil T. Heffernan); (7) Does Self-Discipline impact students' knowledge and learning? (Yue Gong, Dovan Rai, Joseph E. Beck, and Neil T. Heffernan); (8) Consistency of Students' Pace in Online Learning (Arnon Hershkovitz and Rafi Nachmias); (9) Student Consistency and Implications for Feedback in Online Assessment Systems (Tara M. Madhyastha and Steven Tanimoto); (10) Edu-mining for Book Recommendation for Pupils (Ryo Nagata, Keigo Takeda, Koji Suda, Junichi Kakegawa, and Koichiro Morihiro); (11) Conditional Subspace Clustering of Skill Mastery: Identifying Skills that Separate Students (Rebecca Nugent, Elizabeth Ayers, and Nema Dean); (12) Determining the Significance of Item Order In Randomized Problem Sets (Zachary A. Pardos and Neil T. Heffernan); (13) Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models (Philip I. Pavlik Jr., Hao Cen, Kenneth R. Koedinger); (14) Detecting and Understanding the Impact of Cognitive and Interpersonal Conflict in Computer Supported Collaborative Learning Environments (David Nadler Prata, Ryan S.J.d. Baker, Evandro d.B. Costa, Carolyn P. Rose, Yue Cui, Adriana M.J.B. de Carvalho); (15) Using Dirichlet priors to improve model parameter plausibility (Dovan Rai, Yue Gong, Joseph E. Beck); (16) Reducing the Knowledge Tracing Space (Steven Ritter, Thomas K. Harris, Tristan Nixon, Daniel Dickison, R. Charles Murray, and Brendon Towle); (17) Automatic Detection of Student Mental Models During Prior Knowledge Activation in MetaTutor (Vasile Rus, Mihai Lintean, and Roger Azevedo); (18) Automatic Concept Relationships Discovery for an Adaptive E-course (Marian Simko, Maria Bielikova); (19) Unsupervised MDP Value Selection for Automating ITS Capabilities (John Stamper and Tiffany Barnes); (20) Recommendation in Higher Education Using Data Mining Techniques (Cesar Vialardi, Javier Bravo Agapito, Leila Shafti, Alvaro and Ortigosa); (21) Developing an Argument Learning Environment Using Agent-Based ITS (ALES) (Safia Abbas and Hajime Sawamura); (22) A Data Mining Approach to Reveal Representative Collaboration Indicators in Open Collaboration Frameworks (Antonio R. Anaya and Jesus G. Boticario); (23) Dimensions of Difficulty in Translating Natural Language into First-Order Logic (Dave Barker-Plummer, Richard Cox, and Robert Dale); (24) Predicting Correctness of Problem Solving from Low-level Log Data in Intelligent Tutoring Systems (Suleyman Cetintas, Luo Si, Yan Ping Xin, and Casey Hord); (25) Back to the future: a non-automated method of constructing transfer models (Ming Feng and Joseph Beck); (26) How do Students Organize Personal Information Spaces? (Sharon Hardof-Jaffe, Arnon Hershkovitz, Hama Abu-Kishk, Ofer Bergman, and Rafi Nachmias); (27) Improving Student Question Classification (Cecily Heiner and Joseph L. Zachary); (28) Why, What, and How to Log? Lessons from LISTEN (Jack Mostow and Joseph E. Beck); (29) Process Mining Online Assessment Data (Mykola Pechenizkiy, Nikola Trcka, Ekaterina Vasilyeva, Wil van der Aalst, and Paul De Bra); (30) Obtaining Rubric Weights For Assessments By More Than One Lecturer Using A Pairwise Learning Model (J. R. Quevedo and E. Montanes); (31) Collaborative Data Mining Tool for Education (Enrique Garcia, Cristobal Romero, Sebastian Ventura, Miguel Gea, and Carlos de Castro); (32) Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming (Amelia Zafra and Sebastian Ventura); and (33) Visualization of Differences in Data Measuring Mathematical Skills (Lukas Zoubek and Michal Burda). Individual papers contain tables, figures, footnotes, references and appendices.