Teacher-child relationship quality for young children with parent reported language concerns

Previous research has demonstrated the importance of the qualities of the teacher-child relationship on children’s development. Close teacher-child relationships are especially important for children at risk. Positive relationships have been shown to have beneficial effects on children’s social and academic development (Birch & Ladd, 1997; Pianta & Stuhlman, 2004). Children with language difficulties are likely to face increased risks with regard to long term social and academic outcomes. The purpose of the current research was to gain greater understanding of the qualities of teacher-child relationships for young children with parent reported language concerns. The research analyses completed for this thesis involved the use of data from the public-access database of Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC). LSAC is a longitudinal study involving a nationally representative sample of 10,000 Australian children. Data are being collected biennially from 2004 (Wave 1 data collection) until 2010 (Wave 4 data collection). LSAC has a cross-sequential research design involving two cohorts, an infant cohort (0-1 year at age of recruitment) and a kindergarten cohort (4-5 years at age of recruitment). Two studies are reported in this thesis using data for the LSAC Kindergarten Cohort which had 4983 child participants at recruitment. Study 1 used Wave 1 data to identify the differences between teacher-child relationship qualities for children with parent reported language concerns and their peers. Children identified by parents for whom concerns were held about their receptive and expressive language, as measured by items from the Parents’ Evaluation of Developmental Status (PEDS) (Glascoe, 2000) were the target (at risk) group in the study (n = 210). A matched case control group of peers (n = 210), matched on the child characteristics of sex, age, cultural and linguistic differences (CALD), and socio-economic positioning (SEP), were the comparison group for this analysis. Teacher-child relationship quality was measured by teacher reports on the Closeness and Conflict scales from the short version of the Student-Teacher Relationship Scale (STRS) (Pianta, 2001). There were statistically significant differences in the levels of closeness and conflict between the two groups. The target group had relationships with their teachers that had lower levels of closeness and higher levels of conflict than the control group. Study 2 reports analyses that examined the stability of the qualities of the teacher-child relationships at Wave 1 (4-5 years) and the qualities of the teacher-child relationships at Wave 2 (6-7 years). This time frame crosses the period of the children’s transition to school. The study examined whether early patterns in the qualities of the teacher-child relationship for children with parent reported language concerns at Wave 1 predicted the qualities of the teacher-child relationship outcomes in the early years of formal school. The sample for this study consisted of the group of children identified with PEDS language concerns at Wave 1 who also had teacher report data at Wave 2 (n = 145). Teacher-child relationship quality at Wave 1 and Wave 2 was again measured by the STRS scales of Closeness and Conflict. Results from multiple regression models indicated that teacher-child relationship quality at Wave 1 significantly contributed to the prediction of the quality of the teacher-child relationship at Wave 2, beyond other predictor variables included in the regression models. Specifically, Wave 1 STRS Closeness scores were the most significant predictor for STRS Closeness scores at Wave 2, while Wave 1 STRS Conflict scores were the only significant predictor for Wave 2 STRS Conflict outcomes. These results indicate that the qualities of the teacher-child relationship experienced prior to school by children with parent reported language concerns remained stable across transitions into formal schooling at which time the child had a different teacher. The results of these studies provide valuable insight into the nature of teacher-child relationship quality for young children with parent reported language concerns. These children experienced teacher-child relationships of a lower quality when compared with peers and, additionally, the qualities of these relationships prior to formal schooling were predictive of the qualities of the relationships in the early years of formal schooling. This raises concerns, given the increased risks of poorer social and academic outcomes already faced by children with language difficulties, that these early teacher-child relationships have an impact on future teacher-child relationships. Results of these studies are discussed with these considerations in mind and also discussed in terms of the implications for educational theory, policy and practice.

Gender differences in children's classroom behaviour and teacher-child relationships in early childhood: An Australian Study

Background: In the early school years, children need positive attitudes to school and experiences that promote academic and social competence. Positive relationships between children and teachers make a significant contribution to school achievement and social competence. Girls are more likely to display positive classroom behaviours and positive approaches to learning than boys. Gender differences have also been noted in teacher-child relationships. This study investigated the relationship between gender differences in classroom behaviour and gender differences in teacher-child relationships in the early years. Method: Data were drawn from The Longitudinal Study of Australian Children (LSAC). LSAC is a cross-sequential cohort study funded by the Australian Government. In these analyses, Wave 1 (2004) and Wave 2 (2006) data for 4464 children in the Kindergarten Cohort were used. Children, at Wave 2, were in the early years of formal school. They had a mean age of 6.8 years (SD= 0.24). Measures included a 6-item measure of Approaches to Learning (task persistence, independence) and teacher ratings on the SDQ. Teachers rated their relationships with children on the short form of the STRS. Results: Girls were found to have more positive relationships with their teachers and to display more positive classroom behaviours than boys. Teachers described their relationships with boys as less close than their relationships with girls and rated girls as displaying more positive approaches to learning and fewer problem behaviours than boys. Positive teacher – child relationships were significantly related to more positive classroom behaviours. The quality of the teacher-child relationship at time 1 (Wave 1) was the best predictor of the quality of the teacher-child relationship at time 2 (Wave 2). Conclusions: Findings highlight the importance of developing positive learning related classroom behaviours in understanding successful school transition and the key role played by early positive teacher-child relationships in promoting school adjustment.

How well are Australian children doing in the first year of school : a demographic analysis

Background: The transition to school is a sensitive period for children in relation to school success. In the early school years, children need to develop positive attitudes to school and have experiences that promote academic, behavioural and social competence. When children begin school there are higher expectations of responsibility and independence and in the year one class, there are more explicit academic goals for literacy and numeracy and more formal instruction. Most importantly, children’s early attitudes to learning and learning styles have an impact on later educational outcomes. Method: Data were drawn from The Longitudinal Study of Australian Children (LSAC). LSAC is a cross-sequential cohort study funded by the Australian Government. In these analyses, Wave 2 (2006) data for 2499 children in the Kindergarten Cohort were used. Children, at Wave 2, were in the first year of formal school. They had a mean age of 6.9 years (SD= 0.26). Measures included a 6-item measure of Approaches to Learning (task persistence, independence) and the Academic Rating Scales for language and literacy and mathematical thinking. Teachers rated their relationships with children on the short form of the STRS. Results: Girls were rated by their teachers as doing better than boys on Language and literacy, Approaches to learning; and they had a better relationship with their teacher. Children from an Aboriginal or Torres Strait Island (ATSI) background were rated as doing less well on Language and Literacy and Mathematical thinking and on their Approaches to learning. Children from high Socio Economic Position families are doing better on teacher rated Language and Literacy, Mathematical thinking, Approaches to learning and they had a better relationship with their teacher. Conclusions: Findings highlight the importance of key demographic variables in understanding children’s early school success.

Fusion of in-vehicle sensor data to develop Intelligent Driver Training System (IDTS)

The over represented number of novice drivers involved in crashes is alarming. Driver training is one of the interventions aimed at mitigating the number of crashes that involve young drivers. To our knowledge, Advanced Driver Assistance Systems (ADAS) have never been comprehensively used in designing an intelligent driver training system. Currently, there is a need to develop and evaluate ADAS that could assess driving competencies. The aim is to develop an unsupervised system called Intelligent Driver Training System (IDTS) that analyzes crash risks in a given driving situation. In order to design a comprehensive IDTS, data is collected from the Driver, Vehicle and Environment (DVE), synchronized and analyzed. The first implementation phase of this intelligent driver training system deals with synchronizing multiple variables acquired from DVE. RTMaps is used to collect and synchronize data like GPS, vehicle dynamics and driver head movement. After the data synchronization, maneuvers are segmented out as right turn, left turn and overtake. Each maneuver is composed of several individual tasks that are necessary to be performed in a sequential manner. This paper focuses on turn maneuvers. Some of the tasks required in the analysis of ‘turn’ maneuver are: detect the start and end of the turn, detect the indicator status change, check if the indicator was turned on within a safe distance and check the lane keeping during the turn maneuver. This paper proposes a fusion and analysis of heterogeneous data, mainly involved in driving, to determine the risk factor of particular maneuvers within the drive. It also explains the segmentation and risk analysis of the turn maneuver in a drive.

Twisted Edwards curves revisited

This paper introduces fast algorithms for performing group operations on twisted Edwards curves, pushing the recent speed limits of Elliptic Curve Cryptography (ECC) forward in a wide range of applications. Notably, the new addition algorithm uses for suitably selected curve constants. In comparison, the fastest point addition algorithms for (twisted) Edwards curves stated in the literature use . It is also shown that the new addition algorithm can be implemented with four processors dropping the effective cost to . This implies an effective speed increase by the full factor of 4 over the sequential case. Our results allow faster implementation of elliptic curve scalar multiplication. In addition, the new point addition algorithm can be used to provide a natural protection from side channel attacks based on simple power analysis (SPA).

Characterising anomalous events using change point correlation on unsolicited network traffic

Monitoring unused or dark IP addresses offers opportunities to extract useful information about both on-going and new attack patterns. In recent years, different techniques have been used to analyze such traffic including sequential analysis where a change in traffic behavior, for example change in mean, is used as an indication of malicious activity. Change points themselves say little about detected change; further data processing is necessary for the extraction of useful information and to identify the exact cause of the detected change which is limited due to the size and nature of observed traffic. In this paper, we address the problem of analyzing a large volume of such traffic by correlating change points identified in different traffic parameters. The significance of the proposed technique is two-fold. Firstly, automatic extraction of information related to change points by correlating change points detected across multiple traffic parameters. Secondly, validation of the detected change point by the simultaneous presence of another change point in a different parameter. Using a real network trace collected from unused IP addresses, we demonstrate that the proposed technique enables us to not only validate the change point but also extract useful information about the causes of change points.

Getting skilled for education, training and employment : Indigenous Cultural Knowledges and mainstream knowledges of mathematics

This abstract is a preliminary discussion of the importance of blending of Indigenous cultural knowledges with mainstream knowledges of mathematics for supporting Indigenous young people. This import is emphasised in the documents Preparing the Ground for Partnership (Priest, 2005), The Indigenous Education Strategic Directions 2008–2011 (Department of Education, Training and the Arts, 2007) and the National Goals for Indigenous Education (Department of Education, Employment and Work Relations, 2008). These documents highlight the contextualising of literacy and numeracy to students’ community and culture (see Priest, 2005). Here, Community describes “a culture that is oriented primarily towards the needs of the group. Martin Nakata (2007) describes contextualising to culture as about that which already exists, that is, Torres Strait Islander community, cultural context and home languages (Nakata, 2007, p. 2). Continuing, Ezeife (2002) cites Hollins (1996) in stating that Indigenous people belong to “high-context culture groups” (p. 185). That is, “high-context cultures are characterized by a holistic (top-down) approach to information processing in which meaning is “extracted” from the environment and the situation. Low-context cultures use a linear, sequential building block (bottom-up) approach to information processing in which meaning is constructed” (p.185). In this regard, students who use holistic thought processing are more likely to be disadvantaged in mainstream mathematics classrooms. This is because Westernised mathematics is presented as broken into parts with limited connections made between concepts and with the students’ culture. It potentially conflicts with how they learn. If this is to change the curriculum needs to be made more culture-sensitive and community orientated so that students know and understand what they are learning and for what purposes.

How well are Australian children doing in the first year of school?

Background: The transition to school is a sensitive period for children in relation to school success. In the early school years, children need to develop positive attitudes to school and have experiences that promote academic, behavioural and social competence. When children begin school there are higher expectations of responsibility and independence and in the year one class, there are more explicit academic goals for literacy and numeracy and more formal instruction. Most importantly, children’s early attitudes to learning and learning styles have an impact on later educational outcomes. Method: Data were drawn from The Longitudinal Study of Australian Children (LSAC). LSAC is a cross-sequential cohort study funded by the Australian Government. In these analyses, Wave 2 (2006) data for 2499 children in the Kindergarten Cohort were used. Children, at Wave 2, were in the first year of formal school. They had a mean age of 6.9 years (SD= 0.26). Measures included a 6-item measure of Approaches to Learning (task persistence, independence) and the Academic Rating Scales for language and literacy and mathematical thinking. Teachers rated their relationships with children on the short form of the STRS. Results: Girls were rated by their teachers as doing better than boys on Language and literacy, Approaches to learning; and they had a better relationship with their teacher. Children from an Aboriginal or Torres Strait Island (ATSI) background were rated as doing less well on Language and Literacy and Mathematical thinking and on their Approaches to learning. Children from high Socio Economic Position families are doing better on teacher rated Language and Literacy, Mathematical thinking, Approaches to learning and they had a better relationship with their teacher. Conclusions: Findings highlight the importance of key demographic variables in understanding children’s early school success.

Current evidence on integrated treatment for serious mental disorder and substance misuse

Substance misuse in people with serious mental disorders is common and has a wideranging negative impact. The multiplicity of problems suggests that this comorbidity is better conceptualized as a type of complex disorder than by ‘dual diagnosis’. Problems with sequential and parallel treatments have led to the development of integrated approaches, with one practitioner or team addressing both the substance use and mental disorder. These treatments are typically characterized by motivation enhancement, minimizing treatment-related stress, emphasizing harm reduction as well as abstinence, and assertive outreach. A review of published randomized trials demonstrates that superior effects to controls are rarely consistent across treatment foci and over time. While motivational interventions assist engagement, more intervention is usually required for integrated treatment programs to improve long-term outcomes more than control conditions. More intensive case management does not consistently improve impact, but extended cognitive-behavioral therapies have promise. Suggestions for maximizing treatment effects and improving research evidence are provided.

Residential sales by auction : a property type or geographic consideration

The sale of residential property by auction is a preferred sale method by real estate agents, but not always preferred by some vendors and most residential property buyers. In many residential property markets, the performance and measure of residential property market activity is based on the number of properties offered for sale by auction, auction clearance rates and the number of properties sold prior to auction. However, in many specific residential property markets, sale by auction may not be the preferred or supported method of sale. This paper will review the type of residential property sale within the Sydney residential property market and track the auction sales and clearance rates for Sydney over the past 5 months and compare these results in relation to clearance rates, passed in sales, and properties sold prior to auction This will provide a breakdown of real estate agency sale practice over a large metropolitan region to determine the impact of geographic location and socio-economic factors on the auction of residential property. In addition the paper will analyse the weekly auction sales in the Sydney residential property market to determine what areas of Sydney have the greatest number of house auctions and the performance of the auctions in relation location and socio economic factors.

Investigating customer click through behaviour with integrated sponsored and nonsponsored results

This paper reports findings from a study investigating the effect of integrating sponsored and nonsponsored search engine links into a single web listing. The premise underlying this research is that web searchers are chiefly interested in relevant results. Given the reported negative bias that web searchers have concerning sponsored links, separate listings may be a disservice to web searchers as it might not direct them to relevant websites. Some web meta-search engines integrate sponsored and nonsponsored links into a single listing. Using a web search engine log of over 7 million interactions from hundreds of thousands of users from a major web meta-search engine, we analysed the click-through patterns for both sponsored and nonsponsored links. We also classified web queries as informational, navigational and transactional based on the expected type of content and analysed the click-through patterns of each classification. The findings show that for more than 35% of queries, there are no clicks on any result. More than 80% of web queries are informational in nature and approximately 10% are transactional, and 10% navigational. Sponsored links account for approximately 15% of all clicks. Integrating sponsored and nonsponsored links does not appear to increase the clicks on sponsored listings. We discuss how these research results could enhance future sponsored search platforms.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Empowerment for lifelong learning : embedding information literacy into the business curriculum

Success in modern business demands effective information literacy to address the ever-changing business context. This context includes changes in Government policy reflected through legislation and regulations, developments in case law and expectations of professional associations and the public. Students require the skills to continue their own learning beyond the completion of their degree, since learning the subject content of a course alone sufficient. This paper considers the methods utilised to embed information literacy, in the context of generic skills and graduate attributes, into a Business degree’s curriculum. The paper describes how information literacy has been embedded in two sequential third-year Taxation Law courses, allowing for the explicit development of information literacy. Through the development of legal reasoning and research skills, students are empowered to continue their lifelong learning, which successful professional practice demands. The study will draw upon the experience of the course convener in designing, teaching and evaluating the courses, and on students’ experiences as illustrated through evaluation questionnaire responses and interviews. The findings of this study could be relevant to other business courses, especially company law and auditing.