Data analytics and the novice programmer

University of Technology Sydney. Faculty of Engineering and Information Technology.The aptitude of students for learning how to program (henceforth Programming learn-ability) has always been of interest to the computer science education researcher. This issue of aptitude has been attacked by many researchers and as a result, different algorithms have been developed to quantify aptitude using different methods. Advances in online MOOC systems, automated grading systems, and programming environments with the capability of capturing data about how the novice programmer’s behaviour has resulted in a new stream of studying novice programmer, with a focus on data at large scale. This dissertation applies contemporary machine learning based analysis methods on such “big” data to investigate novice programmers, with a focus on novices at the early stages of their first semester. Throughout the thesis, I will demonstrate how machine learning techniques can be used to detect novices in need of assistance in the early stages of the semester. Based on the results presented in this dissertation, a new algorithm to profile novices coding aptitude is proposed and its’ performance is investigated. My dissertation expands the range of exploration by considering the element of context. I argue that the differential patterns recognized among different population of novices is very sensitive to variations in data, context and language; hence validating the necessity of context-independent methods of analyzing the data

Relationship between mother,s personality traits with perfectionism and Academic procrastination in first grade of high school girl student

Introduction: The Aims this ready project has done to determine relationship between mother’s personality traits with perfectionism and academic procrastination in first grade of high school girl students. Group of studying in this search involve all girl student who studied in first grade of high school in Robatkarim city. Methods: this sample chose base on science contained 200 person by simple random sampling method. For collecting data has used of NEO five factor inventory (short form) and Solomon & Roth Blum academic procrastination scale and frost perfectionism scale. Finding: This project is type of correlation. For data analyze has used describing statistic and perennial statistic person correlation and multi variable Regression step by step and was used. Results: There is significant positive correlation between academic procrastination and mother (neuroticism) and negative or relation with (extraversion, openness, agreeableness, consciententiousness). Also analyze regression showed that mother’s conscientiousness, agreeableness, openness are suitable predictor for academic procrastination. Conclusion: Some of the mother, personality traits, are predictors for perfectionism & girls academic procrastination .This subject confirm more than before intending parent-finials relationship interaction, especially Mother in shape some behavioral characteristic

The Effectiveness of TranscranialDirect Current Stimulation (TDCS) on Anxiety, Depression, and Physical Symptoms of People with Chronic Pain

Introduction: Chronic pain and the experience of living with it are unique. Individual perception of pain is affected by physical, psychological, and social variables. This study aimed to determine the effectiveness of transcranial direct current stimulation (tDCS) on anxiety and depression and physical symptoms of patients with chronic pain.Method: This study was a semi-experimental study with pre-test, post-test, and follow-up with the control group. The statistical population of the study included all outpatients of men and women referred to all medical centers in Tehran in 2019 who had received a definitive diagnosis of chronic pain by neurologists, rheumatologists and psychiatrists, and other relevant specialists. The sampling method in this study had two stages: first, through available sampling, selected from several women with a definite diagnosis of chronic pain, and then, based on inclusion criteria and conducting a pre-test session with interviews. A clinical trial conducted by a researcher under the supervision of a psychiatrist based on DSM-5 diagnostic criteria, 30 women (15 for each group) of women who had the highest scores (as baseline) after completion The questionnaires were selected by simple random sampling method and after random allocation, they were replaced in two groups of intervention and control. Data were obtained using the Beck depression questionnaire and the Beck anxiety questionnaire. Repeated variance analysis was used to analyze the data. The above analysis was carried out using SPSS.22 software.Results: The results showed that the effectiveness of transcranial direct current stimulation (tDCS) was effective on anxiety and depression and physical symptoms of people with chronic pain.Conclusion: It can be concluded that transcranial direct current stimulation (tDCS) is effective in reducing anxiety, depression, and physical symptoms of people with chronic pain and can be used to improve psychological problems in people with chronic pain

Typing Patterns and Authentication in Practical Programming Exams

In traditional programming courses, students have usually been at least partly graded using pen and paper exams. One of the problems related to such exams is that they only partially connect to the practice conducted within such courses. Testing students in a more practical environment has been constrained due to the limited resources that are needed, for example, for authentication. In this work, we study whether students in a programming course can be identified in an exam setting based solely on their typing patterns. We replicate an earlier study that indicated that keystroke analysis can be used for identifying programmers. Then, we examine how a controlled machine examination setting affects the identification accuracy, i.e. if students can be identified reliably in a machine exam based on typing profiles built with data from students' programming assignments from a course. Finally, we investigate the identification accuracy in an uncontrolled machine exam, where students can complete the exam at any time using any computer they want. Our results indicate that even though the identification accuracy deteriorates when identifying students in an exam, the accuracy is high enough to reliably identify students if the identification is not required to be exact, but top k closest matches are regarded as correct.Peer reviewe

Performance and Consistency in Learning to Program

Performance and consistency play a large role in learning. Decreasing the effort that one invests into course work may have short-term benefits such as reduced stress. However, as courses progress, neglected work accumulates and may cause challenges with learning the course content at hand. In this work, we analyze students' performance and consistency with programming assignments in an introductory programming course. We study how performance, when measured through progress in course assignments, evolves throughout the course, study weekly fluctuations in students' work consistency, and contrast this with students' performance in the course final exam. Our results indicate that whilst fluctuations in students' weekly performance do not distinguish poor performing students from well performing students with a high accuracy, more accurate results can be achieved when focusing on the performance of students on individual assignments which could be used for identifying struggling students who are at risk of dropping out of their studies.Peer reviewe

Exploring Machine Learning Methods to Automatically Identify Students in Need of Assistance

ABSTRACT Methods for automatically identifying students in need of assistance have been studied for decades. Initially, the work was based on somewhat static factors such as students' educational background and results from various questionnaires, while more recently, constantly accumulating data such as progress with course assignments and behavior in lectures has gained attention. We contribute to this work with results on early detection of students in need of assistance, and provide a starting point for using machine learning techniques on naturally accumulating programming process data. When combining source code snapshot data that is recorded from students' programming process with machine learning methods, we are able to detect high-and low-performing students with high accuracy already after the very first week of an introductory programming course. Comparison of our results to the prominent methods for predicting students' performance using source code snapshot data is also provided. This early information on students' performance is beneficial from multiple viewpoints. Instructors can target their guidance to struggling students early on, and provide more challenging assignments for high-performing students. Moreover, students that perform poorly in the introductory programming course, but who nevertheless pass, can be monitored more closely in their future studies

Haplotype analysis of hemochromatosis gene polymorphisms in chronic hepatitis C virus infection : A case control study

Funding/Support: This study was financially supported by the research council of Mashhad University of Medical Sciences, Mashhad, Iran (Grant No. 901012).Peer reviewedPublisher PD

Transcriptional drug repositioning and cheminformatics approach for differentiation therapy of leukaemia cells.

Differentiation therapy is attracting increasing interest in cancer as it can be more specific than conventional chemotherapy approaches, and it has offered new treatment options for some cancer types, such as treating acute promyelocytic leukaemia (APL) by retinoic acid. However, there is a pressing need to identify additional molecules which act in this way, both in leukaemia and other cancer types. In this work, we hence developed a novel transcriptional drug repositioning approach, based on both bioinformatics and cheminformatics components, that enables selecting such compounds in a more informed manner. We have validated the approach for leukaemia cells, and retrospectively retinoic acid was successfully identified using our method. Prospectively, the anti-parasitic compound fenbendazole was tested in leukaemia cells, and we were able to show that it can induce the differentiation of leukaemia cells to granulocytes in low concentrations of 0.1 μM and within as short a time period as 3 days. This work hence provides a systematic and validated approach for identifying small molecules for differentiation therapy in cancer

QueryVis: Logic-based diagrams help users understand complicated SQL queries faster

Understanding the meaning of existing SQL queries is critical for code maintenance and reuse. Yet SQL can be hard to read, even for expert users or the original creator of a query. We conjecture that it is possible to capture the logical intent of queries in \emph{automatically-generated visual diagrams} that can help users understand the meaning of queries faster and more accurately than SQL text alone. We present initial steps in that direction with visual diagrams that are based on the first-order logic foundation of SQL and can capture the meaning of deeply nested queries. Our diagrams build upon a rich history of diagrammatic reasoning systems in logic and were designed using a large body of human-computer interaction best practices: they are \emph{minimal} in that no visual element is superfluous; they are \emph{unambiguous} in that no two queries with different semantics map to the same visualization; and they \emph{extend} previously existing visual representations of relational schemata and conjunctive queries in a natural way. An experimental evaluation involving 42 users on Amazon Mechanical Turk shows that with only a 2--3 minute static tutorial, participants could interpret queries meaningfully faster with our diagrams than when reading SQL alone. Moreover, we have evidence that our visual diagrams result in participants making fewer errors than with SQL. We believe that more regular exposure to diagrammatic representations of SQL can give rise to a \emph{pattern-based} and thus more intuitive use and re-use of SQL. All details on the experimental study, the evaluation stimuli, raw data, and analyses, and source code are available at https://osf.io/mycr2Comment: Full version of paper appearing in SIGMOD 202

Comparison of Interferon-Gamma (IFNG) +874 T/A single nucleotide polymorphism in hepatitis C virus infected patients and non-infected controls in Mashhad, Iran

This study was financially supported by Mashhad University of Medical Sciences, Mashhad, Iran (Grant No. 900513).Peer reviewedPublisher PD