The development and application of text-focused methods for evaluating accounting narratives, with a view to investigating impression management

This study responds to a call in the literature for methodological and empirical studies to advance research into accounting narratives. The primary contribution is methodological, drawing on the literature of applied linguistics and that of managerial business communications, in developing for accounting applications three text-focused methods for evaluating accounting narratives. This expansion in the portfolio of approaches available to the accounting researcher offers the potential for a richer empirical analysis, demonstrated in this study through an illustrative empirical application. The methods are developed in light of acknowledged areas of weakness and gaps in the accounting literature and with a view to investigating impression management. A general line of critique in the accounting literature points to a lack of emphasis on the syntactic dimension, with a particular focus on the weaknesses of readability formulas as the dominant method of syntactic analysis. The particular orientation towards the investigation of impression management recognises the increasing importance in the literature of issues associated with impression management in accounting narratives. The aptitude of the methods developed for investigating impression management is demonstrated through an illustrative empirical application in tests of differentiation between `good performers' and `poor performers'. A texture index and a transitivity index go some way towards redressing the general lack of emphasis on the syntactic dimension, exhibited in the existing portfolio of approaches. The texture index is developed as an alternative to readability formulas, is response to the particular focus of critique. The texture index analyses text across a number of dimensions or indexicals and embodies a number of features, which render it attractive to accounting researchers. The transitivity index measures the number of passive constructions in a text, a textual dimension associated with causation and attribution, with a particular relevance to the investigation of impression management. The third approach outlined in this study is DICTION analysis, a computerised content analysis program, which examines a text for its verbal tone, measured across five variables: `certainty', `optimism', `activity', `realism' and `commonality'. This approach is selected principally because of its relevance and applicability to the investigation of impression management. The texture index is drawn from the applied linguistics literature. It has not previously been used in an accounting related application. The transitivity index and DICTION analysis are developed from the managerial business communications literature where both approaches have been applied, albeit to a limited extent, in accounting applications. Both of these approaches have a sound theoretical basis in linguistics. In developing these approaches from the managerial business communications literature, there are two main areas of contribution. First, the methods developed here have hitherto only been exploited to a limited extent in accounting applications. This study advocates the development of the methods in accounting related applications towards their full potential. Second, the methods are developed and adapted as appropriate with the expressed intention of investigating impression management in accounting narratives. In addition to the methodological contribution, the study also yields an empirical contribution through the empirical application. The study finds mixed results in relation to an investigation of differential reporting patterns in the Chairman's statement and `OFR type' Manager's report of `good performing' and `poor performing' investment trust companies. Extending the analysis beyond the traditional focus on the Chairman's statement to include the Manager's report, recognises the increasing importance of such `OFR type' documents and the relative lack of attention they have received hitherto from accounting researchers. The results are reported in light of a detailed synthesis of the empirical impression management literature that is included in this study. As far as the author is aware, this is the first detailed review of this nature in the literature. The study also finds mixed results in relation to differentiation between the Chairman's statement and Manager's report. Finally, the study fosters an ethos of interdisciplinarity between research communities in accounting and the communities of applied linguistics and managerial business communications. Such interdisciplinarity offers the accounting researcher insights and usable methods of analysis, developed in disciplines whose specialism is the evaluation of narrative

MODELING MULTIPLE SOURCE USE: USING LEARNER CHARACTERISTICS AND SOURCE USE BEHAVIORS TO PREDICT RESPONSE QUALITY

Multiple source use (MSU) has been identified as both a critical competency and a key challenge for today's students, living in the digital age (Goldman & Scardamalia, 2013b). Theoretical models of multiple source use provide insights into how the MSU process unfolds and identify points at which students may encounter challenges (i.e., in source selection, processing, and evaluation, Rouet & Britt). However, understandings of MSU have been limited by two gaps in the literature. First, while points of challenge in students' MSU process have been examined independently, comprehensive models considering the joint role of source selection, processing, and evaluation in task performance have not been fully investigated. Further, while research on MSU has focused on students' behaviors when engaging with texts, individual difference factors have been considered only to a limited extent, despite their theorized importance (Rouet, 2006). The purpose of the present study was to examine the extent to which multiple source use behaviors (i.e., source selection, processing, and evaluation) and learner characteristics (i.e., prior knowledge, domain general source evaluation behaviors, stances on the target issue) predicted open-ended task performance, both independently and in conjunction with one another. Participants were 197 undergraduate students, asked to complete measures assessing their prior knowledge, stances on the Arab Spring in Egypt, the topic of the task, and domain general source evaluation behaviors. Then, participants were tasked with using a library of six sources to respond to a controversial prompt about a contemporary event (i.e., Arab Spring in Egypt). While students engaged with sources, log data of source use were collected (e.g., number of sources accessed, time on texts) and participants were asked to rate sources accessed in terms of trustworthiness, usefulness, and interestingness. Four indices were used to assess open-ended response quality: (a) word count, (b) the number of arguments included in students' responses, (c) scores on the SOLO taxonomy (Biggs & Collis, 1982), reflecting the extent to which students' responses integrated and evaluated information presented across texts, and (d) the number of citations in students' answers. Key findings included the role of students' ratings of source interestingness and time on texts as predictive of open-ended task performance. Further, students' accessing of document information about sources (e.g., author credentials that may aid in source evaluation, Britt & Aglinskas, 2002) and trustworthiness evaluations were found to be associated with SOLO scores. Overall, as compared to multiple source use behaviors, learner characteristics were found to have a more limited effect on task performance. Findings are discussed and implications for theoretical conceptions of multiple source use and instructional practice are presented

Learning Analytics Through Machine Learning and Natural Language Processing

The increase of computing power and the ability to log students’ data with the help of the computer-assisted learning systems has led to an increased interest in developing and applying computer science techniques for analyzing learning data. To understand and investigate how learning-generated data can be used to improve student success, data mining techniques have been applied to several educational tasks. This dissertation investigates three important tasks in various domains of educational data mining: learners’ behavior analysis, essay structure analysis and feedback providing, and learners’ dropout prediction. The first project applied latent semantic analysis and machine learning approaches to investigate how MOOC learners’ longitudinal trajectory of meaningful forum participation facilitated learner performance. The findings have implications on refining the courses’ facilitation methods and forum design, helping improve learners’ performance, and assessing learners’ academic performance in MOOCs. The second project aims to analyze the organizational structures used in previous ACT test essays and provide an argumentative structure feedback tool driven by deep learning language models to better support the current automatic essay scoring systems and classroom settings. The third project applied MOOC learners’ forum participation states to predict dropouts with the help of hidden Markov models and other machine learning techniques. The results of this project show that forum behavior can be applied to predict dropout and evaluate the learners’ status. Overall, the results of this dissertation expand current research and shed light on how computer science techniques could further improve students’ learning experience