The Problem Solving Genome: Analyzing Sequential Patterns of Student Work with Parameterized Exercises

Parameterized exercises are an important tool for online assessment and learning. The ability to generate multiple versions of the same exercise with different parameters helps to support learning-by-doing and decreases cheating during assessment. At the same time, our experience using parameterized exercises for Java programming reveals suboptimal use of this technology as demonstrated by repeated successful and failed attempts to solve the same problem. In this paper we present the results of our work on modeling and examining patterns of student behavior with parameterized exercises using the Problem Solving Genome, a compact encapsulation of individual behavior patterns. We started with micro-patterns (genes) that describe small chunks of repetitive behavior and constructed individual genomes as frequency profiles that show the dominance of each gene in individual behavior. The exploration of student genomes revealed the individual genome is considerably stable, distinguishing students from their peers. Using the genome, we were able to analyze student behavior on the group level and identify genes associated with good and poor learning performance

Academic misconduct in higher education: perceptions, self-reports and perspectives

In the last two decades the international research literature has demonstrated a growing awareness of student cheating, with high levels of self-reported cheating, especially in the United states (U.S.). Much of the early literature on student cheating originated in the U.S. but from the mid 1990s onwards there was increased interest in student cheating in Europe and the rest of the world. The aim of this research was to explore perceptions and self-reports of, and attitudes towards, cheating in undergraduate programmes. There was an element of comparison involved, in trying to identify differences between students studying for degrees in healthcare professions and psychology. A mixed methods approach was adopted. First, students (n=159) completed anonymous questionnaires that invited them to i) rate the perceived frequency of use in "students on a course like theirs" of each of 27 behaviours that ranged from signing as present students who were absent from classes to copying in examinations; іі) self-report their own use of the same behaviours. Second, volunteer students (n=10) and academics (n=12) from the same programmes as the questionnaire sample were interviewed. Questionnaires were analysed using SPSS to identify within-group and between-group differences; interview transcripts were analysed using the constant comparative method (Glaser & Strauss, 1967). Ninety six percent of the sample believed that "students on a course like theirs" cheated in some way, exact percentages ranging from 24%-96%, depending on the cheating behaviour. When it came to self-reporting, the students in the sample self- reported significantly less cheating than they perceived in their peers. Whilst there were significant differences between healthcare and psychology students in their perceptions of cheating, no such difference was found in their self-reports. Interviews revealed that almost 60% of students believed that academics rarely investigate suspicions of cheating. Fifty percent of academics confirmed that view

WHAT FACTORS AFFECT CHEATING IN SECONDARY SCHOOL AND WHY?

Cheating in British secondary schools has not been previously researched. The aim of this thesis was to ascertain what factors affect cheating in secondary school and why? Initially, four questions were posed: "what is cheating?', "when is it wrong to cheat?', 'what role do parents play' and 'what are teacher perceptions of cheating compared with those of students?'. These questions were addressed by studying the perspectives of students, parents and teachers using a mixture of quantitative and qualitative methodologies, involving nearly 1000 respondents in six studies. Two models were developed. The first, a four dimensional model, explained what students thought cheating was. Cheating was perceived to be comprised of the following interrelated dimensions: non-academic and academic behaviours, a temporal component, assessment events and degrees of severity. The second, a decision model, indicated under what circumstances cheating might be right or wrong. Cheating was wrong for respondents who perceived only negative academic associations, whilst It could be right for others, when motives for cheating were perceived to be honourable. Respondents reported the extent to which they were like students in scenarios who were portrayed to have cheated in a variety of ways. Data from parents and teachers were used to test and amplify these models. Students and teachers held similar perceptions regarding cheating frequency, but not severity. Parents held perceptions of cheating that were more extreme than those of students and teachers. The findings of these studies have major implications for those involved in the wider educational environments of the home, school and society. Recommendations are made regarding cun-ent educational testing policies, the promotion of leaming and the reduction of cheating

Measuring academic performance of students in Higher Education using data mining techniques

Educational Data Mining (EDM) is a developing discipline, concerned with expanding the classical Data Mining (DM) methods and developing new methods for discovering the data that originate from educational systems. It aims to use those methods to achieve a logical understanding of students, and the educational environment they should have for better learning. These data are characterized by their large size and randomness and this can make it difficult for educators to extract knowledge from these data. Additionally, knowledge extracted from data by means of counting the occurrence of certain events is not always reliable, since the counting process sometimes does not take into consideration other factors and parameters that could affect the extracted knowledge. Student attendance in Higher Education has always been dealt with in a classical way, i.e. educators rely on counting the occurrence of attendance or absence building their knowledge about students as well as modules based on this count. This method is neither credible nor does it necessarily provide a real indication of a student s performance. On other hand, the choice of an effective student assessment method is an issue of interest in Higher Education. Various studies (Romero, et al., 2010) have shown that students tend to get higher marks when assessed through coursework-based assessment methods - which include either modules that are fully assessed through coursework or a mixture of coursework and examinations than assessed by examination alone. There are a large number of Educational Data Mining (EDM) studies that pre-processed data through the conventional Data Mining processes including the data preparation process, but they are using transcript data as it stands without looking at examination and coursework results weighting which could affect prediction accuracy. This thesis explores the above problems and tries to formulate the extracted knowledge in a way that guarantees achieving accurate and credible results. Student attendance data, gathered from the educational system, were first cleaned in order to remove any randomness and noise, then various attributes were studied so as to highlight the most significant ones that affect the real attendance of students. The next step was to derive an equation that measures the Student Attendance s Credibility (SAC) considering the attributes chosen in the previous step. The reliability of the newly developed measure was then evaluated in order to examine its consistency. In term of transcripts data, this thesis proposes a different data preparation process through investigating more than 230,000 student records in order to prepare students marks based on the assessment methods of enrolled modules. The data have been processed through different stages in order to extract a categorical factor through which students module marks are refined during the data preparation process. The results of this work show that students final marks should not be isolated from the nature of the enrolled module s assessment methods; rather they must be investigated thoroughly and considered during EDM s data pre-processing phases. More generally, it is concluded that Educational Data should not be prepared in the same way as exist data due to the differences such as sources of data, applications, and types of errors in them. Therefore, an attribute, Coursework Assessment Ratio (CAR), is proposed to use in order to take the different modules assessment methods into account while preparing student transcript data. The effect of CAR and SAC on prediction process using data mining classification techniques such as Random Forest, Artificial Neural Networks and k-Nears Neighbors have been investigated. The results were generated by applying the DM techniques on our data set and evaluated by measuring the statistical differences between Classification Accuracy (CA) and Root Mean Square Error (RMSE) of all models. Comprehensive evaluation has been carried out for all results in the experiments to compare all DM techniques results, and it has been found that Random forest (RF) has the highest CA and lowest RMSE. The importance of SAC and CAR in increasing the prediction accuracy has been proved in Chapter 5. Finally, the results have been compared with previous studies that predicted students final marks, based on students marks at earlier stages of their study. The comparisons have taken into consideration similar data and attributes, whilst first excluding average CAR and SAC and secondly by including them, and then measuring the prediction accuracy between both. The aim of this comparison is to ensure that the new preparation process stage will positively affect the final results

How Does Coursework Based Study Affect the Learning of Pupils in Secondary Science Education?

The GCSE science syllabus and curriculum changed considerably between 2005 and 2008. A key specification of coursework which had been identified as encouraging routine completion was replaced, requiring coursework to be completed under exam conditions, while a vocational alternative with increased coursework content was also introduced. I set out in this PhD study, as a 'researching teacher', to ascertain the attitudes of pupils and teachers to GCSE science coursework, and whether there exist any differences in pupil attainment linked to the reform of coursework and GCSE examination. I also have looked at how pupils learn in science through completing coursework as part of an evaluation of the effectiveness of coursework in the GCSE science curriculum. This thesis takes the form of a case study comprising reflecting a practitioner based enquiry using mixed methods methodology. It is therefore an integrated longitudinal design combining qualitative and quantitative methods. Qualitative data was elicited from interviews, questionnaires, observation and field notes. Quantitative analyses were undertaken of pupil performance in coursework and examination results. Key research findings include confirmation that many pupils in the case study preferred a coursework based approach to their science education, and they found they learned more from this approach. Pupils were also found to prefer learning when a constructivist model of teaching and learning was adopted in the classroom. Active learning led to improvements in understanding and completing coursework. Additional analysis of quantitative data showed that many pupils achieved Significantly better grades for their science coursework than they did through examinations. Further, the data revealed when coursework can be improved using an assessment-based approach to learning, and that there Were no Significant statistical differences between boys and girls in coursework and examination results. The research revealed that when coursework for GCSE science is reviewed and improved as part of a constructivist model of learning, there is a positive contribution to attainment levels in the GCSE examination. Furthermore, there is a need to consider how the format of that coursework ensure it does not encourage routine completion, but instead encourages assessment for learning, active learning and individual responsibility for learning. The thesis, overall, represents a personal, scholarly and professional engagement in understanding the work of teaching GCSE science

The Problem Solving Genome: Analyzing Sequential Patterns of Student Work with Parametrerized Exercises

ABSTRACT Parameterized exercises have recently emerged as an important tool for online assessment and learning. The ability to generate multiple versions of the same exercise with different parameters helps to support learning-by-doing and decreases cheating during assessment. At the same time, our experience with using parameterized exercises for Java programming reveals suboptimal use of this technology as demonstrated by repeated successful and failed attempts to solve the same problem. In this paper we present the results of our work on modeling and examining patterns of student behavior with parameterized exercises using Problem Solving Genome, a compact encapsulation of individual behavior patterns. We started with micro-patterns (genes) that describe small chunks of repetitive behavior and constructed individual genomes as frequency profiles that shows the dominance of each gene in individual behavior. The exploration of student genomes revealed that individual genome is very stable, distinguishing students from their peers and changing very little with the growth of knowledge over the course. Using the genome, we were able to analyze student behavior on the group level and identify genes associated with good and bad learning performance

Accelerating Academic Literacy Development: Issues, Possibilities and Challenges for Integrating Scholarly Writing Development into Mainstream Curriculum in Australian Higher Education.

This thesis presents a longitudinal case study of the collaborative integration of a pedagogy for Accelerating Academic Literacy Development (AALD) into a mainstream STEM course at an Australian South Coast University. The distinguishing feature of the pedagogy is a conceptual self-help tool based on principles of genre analysis. The purpose of teaching genre analysis is to empower students to analyse course-specific readings as models for accelerating their own academic literacy development, while a collaborative pedagogy is intended to empower a discipline specialist to adopt the AALD approach and autonomously continue to develop it as her own. This study details the successful and sustained curriculum-integration of the AALD pedagogy into a mainstream semester-length course by two STEM lecturers, each in collaboration with the author. It examines institutional and personal contexts and conditions for possibilities and challenges to a more widespread adoption of a curriculum integrated AALD focus. Findings indicate that the two STEM lecturers derived sufficient confidence from their initial co-teaching, and from their familiarity with the course-specific genre analysis worksheets, to retain, and modify, the AALD module within the same course in subsequent years. Implications of the findings for the scalability of the AALD are discussed in light of current contextual challenges.Thesis (Ph.D.) -- University of Adelaide, School of Education, 202