Comprehensive Induction or Add-on Induction: Impact on Teacher Practice and Student Engagement

In recent years, we have seen a rapid expansion of policies and resources devoted to new teacher induction. Most of these policies are based on an assumption that induction programs have a positive influence on teacher quality and student learning. Yet there is little evidence to support claims for such policies regarding the distinct components of induction programs or their effectiveness (Wang, Odell & Schwille, 2008). Scholars have argued for targeted mentoring that addresses the learning needs of beginning teachers with regard to instructional practice (Feiman-Nemser, 2001). Some suggest that induction efforts may increase teacher knowledge, student achievement, teacher satisfaction, and retention (Darling-Hammond, 1999; Fletcher, Strong & Villar, 2008; Smith & Ingersoll, 2004).There is, however, insufficient data to assist educators and policy makers in determining the most effective or critical components of induction programs. There is scant consensus around a number of induction issues, for example: the most effective mentoring condition (full-time or add-on mentoring); the amount of time required to enhance the development of beginning teachers; the amount of professional development mentors need to be effective; and the level of match (subject or grade level) required between mentor and beginning teacher. Furthermore, few studies explore the different components of induction and their effects on teacher and student outcomes.Given such a dearth of evidence and the current state of induction policy, this study was developed to examine differences in student engagement and teacher instructional practice in two types of induction conditions: comprehensive full-time induction and add-on induction. These two conditions differed in- the amount of mentor participation in professional development on induction;- the amount of time mentors could spend on structured observations, reflection, and feedback focused on pedagogy;- mentors' abilities to prioritize induction efforts;- mentors' abilities to serve as liaisons between beginning teachers and administrators; and- the amount of professional development mentors could offer beginning teachers.The goal of this study was to examine the instructional practice of beginning teachers who were mentored in these two conditions and to explore differences in instructional practice and student engagement

Evaluating site induction practice efficiency and effectiveness:an organisational case study

The style of induction presentation and other processes, irrespective of duration, immediately establishes the context and attitude of the construction site team and is where initial behavioural standards are established. A case study within a large contractor investigates site induction activities in practice to better understand the operational demands on time for those involved in managing site inductions and the impact of this activity on safety behaviour on site. The research method adopted was a desk-based review of company policy through document analysis, observations of site induction practice, operations and semi structured interviews. Trade-offs between time losses/benefits, safety in practice, technology implementation and their impact on administrative processes are examined. It is argued that the use of observations has allowed the identification of the actual time commitment in practice. The principal contractor's allocated time for providing and undertaking site induction activities was underestimated by 16% to 20%. There is potential to save time through exploitation of existing and new technology solutions more fully. However, those with an H&S leadership role have indicated difficulties in keeping up with the pace of change in technology development for this purpose

Rapid Sequence Induction Versus Traditional Induction: An Objective Structured Clinical Exam

Every surgical procedure requiring general anesthesia begins with the induction process. Induction of general anesthesia can be altered by using a number of combinations of pharmacological agents and airway management techniques to place the patient in an anesthetic state while providing adequate oxygenation. Mastering the induction process as well as airway management are integral parts of being a competent anesthesia provider. Twenty million endotracheal tubes are placed within the United States annually by medical professionals (Grant, 2013). Endotracheal tube placement can be a stressful process for anesthesia students to master. The aim of this doctoral project is to provide anesthesia students and providers current evidence-based information on general anesthesia induction and rapid sequence induction processes. The doctoral project investigators conducted research compiling current up to date literature on the general induction processes. This objective structured clinical exam (OSCE) allows students to simulate the induction processes within a low-stress environment before experiencing the clinical setting. The research helped mold this observed structured clinical exam as well as a post-examination anonymous survey. Feedback collected from the surveys helped modify this doctoral project for future anesthesia providers. Sixteen participants including four expert panelists and twelve current students within The University of Southern Mississippi Nurse Anesthesia Program provided feedback through the form of a survey. The survey results showed all sixteen participants felt that information provided within the OSCE was evidence based and currently the standard of practice. Current anesthesia providers and students alike, expressed the idea that providing this doctoral project to anesthesia students would aid in readiness for the clinical setting

Structured Attention for Unsupervised Dialogue Structure Induction

Inducing a meaningful structural representation from one or a set of dialogues is a crucial but challenging task in computational linguistics. Advancement made in this area is critical for dialogue system design and discourse analysis. It can also be extended to solve grammatical inference. In this work, we propose to incorporate structured attention layers into a Variational Recurrent Neural Network (VRNN) model with discrete latent states to learn dialogue structure in an unsupervised fashion. Compared to a vanilla VRNN, structured attention enables a model to focus on different parts of the source sentence embeddings while enforcing a structural inductive bias. Experiments show that on two-party dialogue datasets, VRNN with structured attention learns semantic structures that are similar to templates used to generate this dialogue corpus. While on multi-party dialogue datasets, our model learns an interactive structure demonstrating its capability of distinguishing speakers or addresses, automatically disentangling dialogues without explicit human annotation.Comment: Long paper accepted by EMNLP 202

Potential Mentoring Impacts on Oklahoma Induction-Year School-Based Agricultural Education Teachers: A Modified Delphi Study

Literature supports benefits of mentoring for induction-year school-based agricultural education (SBAE) teachers. Yet for the past 15 years, no structured mentoring program has been offered for Oklahoma SBAE induction-year teachers. This study sought to find consensus among an expert panel representing Oklahoma SBAE regarding the impact on induction-year SBAE teachers without a structured mentoring program. Panel members were asked to respond to three open-ended questions representing goals, outcomes, and impacts of a mentoring program. Sixty-two unique statements representing eight themes met consensus. Themes included building mentoring relationships, effective emotional management, effective SBAE program management, impact to the profession, student learning, teacher retention, introduction to school climate, and reinforcing effective teaching behaviors. Oklahoma SBAE induction-year teachers and programs are negatively impacted from the lack of a structured mentoring program. The planning, funding, and implementation of a mentoring program for Oklahoma SBAE induction-year teachers should be a focus of professional development

Solving Structured Hierarchical Games Using Differential Backward Induction

Many real-world systems possess a hierarchical structure where a strategic plan is forwarded and implemented in a top-down manner. Examples include business activities in large companies or policy making for reducing the spread during pandemics. We introduce a novel class of games that we call structured hierarchical games (SHGs) to capture these strategic interactions. In an SHG, each player is represented as a vertex in a multi-layer decision tree and controls a real-valued action vector reacting to orders from its predecessors and influencing its descendants' behaviors strategically based on its own subjective utility. SHGs generalize extensive form games as well as Stackelberg games. For general SHGs with (possibly) nonconvex payoffs and high-dimensional action spaces, we propose a new solution concept which we call local subgame perfect equilibrium. By exploiting the hierarchical structure and strategic dependencies in payoffs, we derive a back propagation-style gradient-based algorithm which we call Differential Backward Induction to compute an equilibrium. We theoretically characterize the convergence properties of DBI and empirically demonstrate a large overlap between the stable points reached by DBI and equilibrium solutions. Finally, we demonstrate the effectiveness of our algorithm in finding \emph{globally} stable solutions and its scalability for a recently introduced class of SHGs for pandemic policy making

Causal Induction from Continuous Event Streams: Evidence for Delay-Induced Attribution Shifts

Contemporary theories of Human Causal Induction assume that causal knowledge is inferred from observable contingencies. While this assumption is well supported by empirical results, it fails to consider an important problem-solving aspect of causal induction in real time: In the absence of well structured learning trials, it is not clear whether the effect of interest occurred because of the cause under investigation, or on its own accord. Attributing the effect to either the cause of interest or alternative background causes is an important precursor to induction. We present a new paradigm based on the presentation of continuous event streams, and use it to test the Attribution-Shift Hypothesis (Shanks & Dickinson, 1987), according to which temporal delays sever the attributional link between cause and effect. Delays generally impaired attribution to the candidate, and increased attribution to the constant background of alternative causes. In line with earlier research (Buehner & May, 2002, 2003, 2004) prior knowledge and experience mediated this effect. Pre-exposure to a causally ineffective background context was found to facilitate the discovery of delayed causal relationships by reducing the tendency for attributional shifts to occur. However, longer exposure to a delayed causal relationship did not improve discovery. This complex pattern of results is problematic for associative learning theories, but supports the Attribution-Shift Hypothesi

The role of structured induction in expert systems

A "structured induction" technique was developed and tested using a rules- from -examples generator together with a chess -specific application package. A drawback of past experience with computer induction, reviewed in this thesis, has been the generation of machine -oriented rules opaque to the user. By use of the structured approach humanly understandable rules were synthesized from expert supplied examples. These rules correctly performed chess endgame classifications of sufficient complexity to be regarded as difficult by international master standard players. Using the "Interactive ID3" induction tools developed by the author, chess experts, with a little programming support, were able to generate rules which solve problems considered difficult or impossible by conventional programming techniques. Structured induction and associated programming tools were evaluated using the chess endgames Icing and Pawn vs. King (Black -tomove) and King and Pawn vs. King and Rook (White -to -move, White Pawn on a7) as trial problems of measurable complexity.Structured solutions to both trial problems are presented, and implications of this work for the design of expert systems languages are assessed

No Free Lunch versus Occam's Razor in Supervised Learning

The No Free Lunch theorems are often used to argue that domain specific knowledge is required to design successful algorithms. We use algorithmic information theory to argue the case for a universal bias allowing an algorithm to succeed in all interesting problem domains. Additionally, we give a new algorithm for off-line classification, inspired by Solomonoff induction, with good performance on all structured problems under reasonable assumptions. This includes a proof of the efficacy of the well-known heuristic of randomly selecting training data in the hope of reducing misclassification rates.Comment: 16 LaTeX pages, 1 figur