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An Empirical Investigation of the Relationship Between Success in Mathematics and Visual Programming Courses
Abstract
Many universities do not have prerequisites for the introductory computer visual programming course. Therefore, faculty and students do not have any means of predicting the student's performance in this course. This research addresses this issue. Past research and accepted theory are presented to show the cognitive requirements for success in a first procedural programming course to be similar to those required for success in a mathematics course. Such research is lacking for visual programming. This research shows similar correlations between math courses and visual programming courses. Significant positive correlations were found between grades from Freshmen mathematics courses, ACT math scores, SAT math scores and grades from a Sophomore introductory visual programming course. This indicates that students who perform well in Freshman level Math courses, possess the cognitive characteristics required to perform equally well in Sophomore level visual programming classes. We can predict that students who perform well in math courses will perform equally well in a visual programming course.
... This kind of a scenario was also witnessed in various universities and tertiary institutions in America in 1990s. White and Sivitanides (2000), carried out a study of several tertiary institutions and universities in America concerning the relationship between success in mathematics and certain computer programming units by the first year students. From the findings, it was evident that the students who performed well in freshman level mathematics units possessed the cognitive characteristics required to perform equally well in sophomore level computer programming classes. ...
... From the findings, it was evident that the students who performed well in freshman level mathematics units possessed the cognitive characteristics required to perform equally well in sophomore level computer programming classes. As a result, many tertiary institutions in America made mathematics a prerequisite for most of the computer courses (White & Sivitanides, 2000). ...
... Therefore, the arguments by the structured programming lecturers that, the main reason as to why majority of students fail in structured programming examinations is because of their poor performance in secondary school mathematics does not hold. Even though performance in KCSE mathematics has been treated in the literature review as a predictor for performance in higher or tertiary education (White & Sivitanides, 2000;Campbell & Bruce, 2009;Oluoch, 2000), this may not be the case when it comes to performance in Structured Programming unit in the DICT module one level. ...
The poor performance in structured programming unit in Kenya national examinations council (KNEC) examination for diploma in Information Communication Technology (DICT) programme by students in module one has caused a great outcry in most of the Technical Institutes in Kenya. The purpose of this study was to determine the relationship between the students' performance in secondary school mathematics and diploma's structured programming unit in technical institutes within Meru County, Kenya. The target population composed of all the 118 students that had sat for the KNEC examination in structured programming unit from the year 2012 to 2015 in Meru, Kiirua and Nkabune technical institutes, and the six lecturers that had taught them in the structured programming unit. The study established that there is no significant relationship between the students' performance in secondary school mathematics and diploma's structured programming unit in technical institutes within Meru County, Kenya (r = 0.098, p = 0.291, α = 0.05, R2 = 0.010, adjusted R2 = 0.001, f = 1.124). 1.1 Background of the Study Diploma in Information Communication Technology (DICT) is one of the most recent computer programmes that were introduced by the Kenya National Examinations Council (KNEC) in the year 2010. It is classified as a TVET programme that was designed to equip the trainees with knowledge, skills and attitudes to perform system operations, system analysis, software design and development, besides, performing network systems and support duties. It is divided into three modules and takes a period of three years for one to graduate. The first module is composed of seven key units which are structured programming, introduction to ICT and ethics, computer application 1, communication skills, computational
... Problem-solving can be seen as an important competence and is required in algorithmic thinking. Furthermore, as discussed in [34] level of problem-solving skills might be a good predictor of future programming performance. In addition, a positive correlation between programming conceptions, skills, and problem-solving abilities was provided in [22,23,35,36]. ...
... While the literature has shed some light on the impact of preferences and abilities on learning [18,34], the relationship between learning outcomes and factual/contextual inclinations is not broadly examined. For example, those preferences have been shown to enhance abilities in the preferred topic among preschoolers [6,14], but this effect was not found among middle school students [3]. ...
This paper investigates the comprehension of loops among novice
programmers using block-based programming languages. While
previous studies have extensively examined programming misconceptions
in higher education and text-based programming languages,
limited attention has been given to the exploration of such
misconceptions in block-based programming, which is commonly
used in K-12 education. The study analyzes the understanding of
loops with a sample of N=114 lower secondary school students,
focusing on two aspects: (1) identifying error clusters in programming
tasks and (2) relating learning outcomes to students’ abilities
and interests. The results reveal significant challenges in 8th-grade
students’ comprehension of loops in block programming. Additionally,
higher-performing students demonstrate fewer errors in the
programming test, reflecting their problem-solving skills. However,
no significant correlation is found between students’ interest and
their performance on the programming test, indicating that no distinct
interest groups can be identified based on learning outcomes.
The study suggests that providing expository learning materials
differentiated by students’ abilities and interests can help address
conceptual difficulties.
... Problem-solving and logical-mathematical thinking skills are also essential for programming; however, it can also be said that learning computer programming is also an effective way to develop these skills [50]. Analytical and logical thinking skills are also necessary to perform successfully in Mathematics as well as in procedural, object-oriented, and visual programming courses [51]. Multiple studies [52][53][54] have revealed that there is a significant correlation between the students' performance in the programming courses and their mathematics achievement. ...
... This study investigated to see if the same associations arose in our sample. Contrary to previous findings [51], this study found that robotic interaction was only related to programming skills and not math. These findings may be due to more metacognitive factors [54] that are more domain general (critical thinking, self-regulation). ...
With the ongoing digital revolution and the ever-increasing development of new technology, schools must provide students with a grounding in certain technical skills, such as computational thinking, which are likely to be required for future roles, including working with and operating robots. For such robot human interaction to be successful, introduction of robots into school learning environments is crucial. However, we also need to better understand the learning processes involved in this human robot interaction. Acknowledging the importance of metacognitive processes as an essential aspect in achieving learning outcomes in educational contexts, this article investigates the experiences of students in human-robotic interactions and related tasks (programming and math) by exploring the student’s self-assessment of perceived performance (JOP), motivation (fun and difficulty) and learning. This aim has been achieved through a pilot study whereby the authors conducted a three-day workshop with grade 6 students and collected pre and post survey data. The findings contribute knowledge to our understanding of the importance of metacognition and in particular accurate self-assessment as crucial for both motivation and learning with humanoid robots.Keywordsself-assessmentmotivationlearninghumanoid robotsrobot-assisted teaching
... In a study carried out by White G and Sivitanides M. in 2002 [1], it was observed that university students who had a good performance in first-year mathematic courses, had the necessary cognitive characteristics to perform equally well on second-year visual programming classes. Their research further shows that analytical and logical thinking skills are necessary to perform with success, not only learning mathematics, but in learning procedural programming language, object-oriented programming languages and visual programming languages. ...
... Other studies show, from the teaching-learning processes, a relationship between computational and mathematical thinking [1][2][3]. In Computational or mathematical thinking, other processes intervene as well, such as reading and writing [4]. ...
This study intends to determine whether similarities of the functioning of the cerebral cortex exist, modeled as a graph, during the execution of mathematical tasks and programming related tasks. The comparison is done using network parameters and during the development of computer programming tasks and the solution of first-order algebraic equations. For that purpose, electroencephalographic recordings (EEG) were made with a volunteer group of 16 students of systems engineering of Universidad del Norte in Colombia, while they were performing computer programming tasks and solving first-order algebraic equations with three levels of difficulty. Then, based on the Synchronization Likelihood method, graph models of functional cortical networks were developed, whose parameters of Small-Worldness (SWN), global(Eg) and local (El) efficiency were compared between both types of tasks. From this study, it can be highlighted, first, the novelty of studying cortical function during the solution of algebraic equations and during programming tasks; second, significant differences between both types of tasks observed only in the delta and theta bands. Likewise, the differences between simpler mathematical tasks with the other levels in both types of tasks; third, the Brodmann areas 21 and 42, associated with auditory sensory processing, can be considered as differentiating elements of programming tasks; as well as Brodmann area 8, during equation solving.
... Overall, a factor that is frequently associated with learning success in programming seems to be mathematically related skills, but factors that could be seen as responsive to both internal and environmental mechanisms is the degree of confidence (including positive self-assessment) and thinking about learning (metacognition) (Bennedsen & Caspersen, 2005;Byrne & Lyons, 2001;Holvikivi, 2010;White & Sivitanides, 2003). Next, we will discuss the role of internal states on learning improvement and, following that, other potential avenues more specific to programming language learning. ...
... To summarize these findings, we see that our regression models agree with our theoretical conception of the relationships between our variables in that the biggest contributors to programming knowledge formation seems to be confidence driven by working hands-on, NFC, grade average and math grade. This fits with the findings discussed in the introduction where mathematics was proposed as a good candidate for predicting good programming outcomes for students in multiple papers (Bennedsen & Caspersen, 2005;Byrne & Lyons, 2001;Choi-man, 1988;Erdogan et al., 2008;Fincher et al., 2006;Holvikivi, 2010;White & Sivitanides, 2003;Wilson & Shrock, 2001), even though one could predict this to be somewhat task dependent. It is worth noting that both mathematical ability and average grades are often used in research as rough approximations of IQ (Mitchell et al., 2020), and NFC has been shown to be modestly related to fluid intelligence in general (Fleischhauer et al., 2010). ...
Computer programming is fast becoming a required part of School curricula, but students find the topic challenging and university dropout rates are high. Observations suggest that hands-on keyboard typing improves learning, but quantitative evidence for this is lacking and the mechanisms are still unclear. Here we study neural and behavioral processes of programming in general, and Hands-on in particular. In project 1, we taught naïve teenagers programming in a classroom-like session, where one student in a pair typed code (Hands-on) while the other participated by discussion (Hands-off). They were scanned with fMRI 1-2 days later while evaluating written code, and their knowledge was tested again after a week. We find confidence and math grades to be important for learning, and easing of intrinsic inhibitions of parietal, temporal, and superior frontal activation to be a typical neural mechanism during programming, more so in stronger learners. Moreover, left inferior frontal cortex plays a central role; operculum integrates information from the dorsal and ventral streams and its intrinsic connectivity predicts confidence and long-term memory, while activity in Broca’s area also reflects deductive reasoning. Hands-on led to greater confidence and memory retention. In project 2, we investigated the impact of feedback on motivation and reaction time in a rule-switching task. We find that feedback targeting personal traits increasingly impair performance and motivation over the experiment, and we find that activity in precentral gyrus and anterior insula decrease linearly over time during the personal feedback condition, implicating these areas in this effect. These findings promote hands-on learning and emphasize possibilities for feedback interventions on motivation. Future studies should investigate interventions for increasing Need for Cognition, the relationship between computer programming and second language learning (L2), and the role of explicit verbalization of knowledge for successful coding, given the language-like processing of code.
... Fan and Li [57] surveyed 940 students from five schools in Taiwan and found that high school and college mathematics grades are positively associated with introductory college CS grades, although they also found that the relationship between mathematics entrance exam scores and CS grades is not statistically significant. White and Sivitanides [26] used records from 837 students over three years and found that college mathematics course performance is positively correlated with CS grades, even for a visual programming course. ...
... Numerous studies have found that prior computing experience contributed to success in college CS courses [26][27][28][29][30], especially for female students. Holden and Weeden [31] found for 159 college students that, while prior programming experience predicts performance in an introductory CS class, it fails to predict performance in subsequent CS classes. ...
Success in an introductory college computer science (CS) course encourages students to major and pursue careers in computer science and many other STEM fields, whereas weak performance is often a powerful deterrent. This article examines the role of high school course taking (AP, regular, or none) in mathematics and in CS as predictors of later success in college introductory computer science courses, measured by students’ final grades. Using a sample of 9,418 students from a stratified random sample of 118 U.S. colleges and universities, we found that the observed advantage of taking AP calculus over taking AP CS, seen in an uncontrolled model, was largely confounded by students’ background characteristics. After applying multinomial propensity score weighting, we estimated that the effects of taking AP calculus and AP CS on college CS grades were similar. Interestingly, enrollment in both AP calculus and AP CS did not have any additional positive effect, suggesting that both AP calculus and AP CS strengthened similar skills that are important for long-term CS achievement. Taking regular CS did not have a significant effect; taking regular calculus had a positive effect, about half the size of taking AP calculus or AP CS. Thus, the study showed that simply exposing students to any kind of CS course before college does not appear to be sufficient for improving college CS performance; and that advanced CS and advanced calculus in high school may substitute for each other in the preparation of college CS.
... Additionally, mathematics found to have a positive effect or one of the factors that contribute to the success of the students taking computer science program [5]. Hence, students must have a strong background in mathematics in which some studies predict success in programming courses [2], [6], [7]. Works of literature also suggest that math scores on the College Entrance Test correlate with programming grades [7], [8] [9]. ...
... Hence, students must have a strong background in mathematics in which some studies predict success in programming courses [2], [6], [7]. Works of literature also suggest that math scores on the College Entrance Test correlate with programming grades [7], [8] [9]. As such, there is a tendency that students who get high scores in mathematics (through College Entrance Exam) can perform well in programming courses. ...
Students find programming courses difficult in the BSCS program because it includes structures, syntax, critical thinking, and the ability to solve programming problems. On the other hand, Mathematics is found to be significant in dealing with programming courses because it improves the logical ability of the students needed to solve programming problems. Hence, this study attempts to investigate the correlation of mathematical ability and programming ability of a student taking BSCS program. This study used the correlation research design to determine the extent to which two factors are related, not the extent to which one factor causes changes in another factor. This study also used several variables such as the College Entrance Examination results (Non-verbal Stanine) in Mathematics only and their performance in all programming courses from the first-year level up to fourth-year level.
... Because of this, mathematical education is important for computer education, as having mathematical knowledge creates a positive impact on learning computer programming (Ricardo, 1983;Ignatuk, 1986). Additionally, White and Sivitanides (2003) found that mathematics education has effects on structural computer programming and object-based computer programming, and that mathematical knowledge makes a positive contribution to these, while Taylor and Monnfield (1991) found a positive correlation between the level of mathematical knowledge and achievement in structural programming. ...
... SEMT students' results were better than CEIT students in regard to misconceptions about the timeefficiency in algorithms. This supports the finding that people who know mathematical representations can achieve well in constructing a computer-language mentality (Ricardo, 1983;Ignatuk, 1986;Taylor & Monnfield, 1991), even in object-based computer programming (White & Sivitanides, 2003). This study emphasizes the importance of mathematicians' analytical thinking. ...
This study focuses on how students in vocational high schools and universities interpret the algorithms in structural computer programming that concerns time-efficiency. The targeted research group consisted of 242 students from two vocational high schools and two departments of the Faculty of Education in Istanbul. This study used qualitative and quantitative research methods together. During the study the students were asked to evaluate the algorithms given to them in terms of their run-time efficiency. The analysis of the data has used descriptive statistics and the results of the independent sample T-tests to compare the groups. The findings of this study slightly stress that the students have misconceptions about algorithms. Students in high schools and in computer education and instructional education departments have the same observed misconceptions, while mathematics education students are better at interpreting the misconceptions.
... If somebody wants to pursue his career in the engineering field, the mathematics' importance has remained quite constant so far. A number of studies by [32], [33], [34] confirmed that mathematical knowledge is a strong predictor of success in programming. ...
21st-century skills such as critical thinking, problem-solving, and analytical thinking gained importance to survive in today’s world. There is growing research mostly focus on the prediction of students in higher education using machine learning and statistical models. However, predicting primary and middle school student’s performance also becomes important especially in learning computer programming. In this study, it was primarily proposed to a fuzzy logic system to predict student performance during the experiment then compare fuzzy logic prediction results to the experts’ results. Secondly, to test the theory that students’ interest in learning algorithms and coding can be increased using the creation of games in a visual programming tool for beginners. The fuzzy logic inference system has been employed to predict middle school student’s performance in the programming experiment which has been carried out using the Scratch environment with the participation of three different middle school students in Turkey. The success rate of three different middle school group success rates is estimated regarding task completion times, and the regression results with respect to the groups are %80, %97, %84.
... The computer science (CS) programme stands out as one of the most challenging programmes due to its dual demands: a fully comprehensive understanding of the theoretical background of computer science and an improved ability to put it into practice in order to meet all the academic requirements (Wu et al., 2014). Many students encounter difficulties with mathematical subjects, and the strong correlation between success in programming courses and performance in mathematical courses remains prevalent (Ali et al., 2014;Balmes, 2017;White and Sivitanides, 2015). ...
Students’ dropout of Computer Science (CS) education is a crucial issue. This study aims to investigate one of the aspects which can help to retain our students. It is vital to examine CS education on the challenge of competence transition within the BSc curriculum from faculty perspectives. Teachers’ expectations about students’ skills and knowledge are important to understand because they influence learning outcomes and teaching methodologies. Acquiring hard skills and professional skills has outstanding significance in preparing students for their future careers. This study uses Q-methodology to identify the different viewpoints on the skills necessary to obtain a CS degree. Teachers from CS bachelor’s programmes at a large European university participated in the study and shared their opinions. The participants were asked to rank the statements along a spectrum of “the most important skill” to “unimportant skill” (containing hard skills as well as soft skills). Factor analysis revealed five factors that are key components to obtaining a degree in CS: 1. analytical and technical skills, 2. teamwork and self-study experience, 3. group programming experience, 4. communication and problem-solving skills, 5. mathematical foundations and process modelling ability. This exploratory study applied a new research instrument and approach to incorporate teachers’ perspectives into research and practice. These findings could help administrators develop new curricula in order to increase students’ retention. We confirmed the need for acquiring professional skills and highlighted the need for designing new programmes which can improve students’ soft skills to prepare them for work in the IT field.
... Although the students have low motivation, they can grasp the complex operations and principles of programming more easily thanks to their current mathematical knowledge and experience. In the literature, it is stated that mathematical knowledge is a significant predictor of achievement in a programming course (Ninrutsirikun et al., 2020;White & Sivitanides, 2003). ...
Background
Studies on the effectiveness of block‐based environments continue to produce inconsistent results. A strong reason for this is that most studies compare environments that are not equivalent to each other or to the level of learners. Moreover, studies that present evidence of the effectiveness of block‐based environments by comparing equivalent environments are limited.
Objectives
This study aims to scrutinize the effects of programming training to be held in equivalent environments (block‐based and text‐based) with university students who do not have prior programming knowledge and experience on achievement, logical thinking, and motivation.
Methods
The study was conducted by using an experimental pretest‐posttest control group design. The study was conducted with 60 students, the total consisting of 30 students in the experimental group and 30 students in the control group. In the experimental group, block‐based visual programming training with Scratch was conducted and the control group received text‐based programming training with Small Basic. The training was maintained for 10 weeks, for 4 h a week in each group. The programming achievement test, the logical thinking skills test, and the motivation scale were used to collect the data.
Results and Conclusions
The results showed that the use of a block‐based environment in programming training contributed positively to the development of students' logical thinking skills, and motivation for learning programming. In contrast, there was evidence that this training did not make a difference on programming success.
Implications
The findings of the study provided evidence of the effectiveness of block‐based training in comparisons made in equivalent environments. Focusing research on this issue may contribute to the improvement of the current understanding.
... This might lead to important savings for the government and students alike (Bernardo et al., 2017). Analysing the problem at a deeper level, preventive actions can be made which could generate great benefits (Aljohani, 2016;White and Sivitanides, 2015). Tinto and Cullen (1973) suggested a comprehensive model to explain dropout and it was adopted in the higher education model by Tinto (1990). ...
Currently, the dropout rate is crucial in the field of Computer Science (CS) higher education. In CS education it is usually the mathematically oriented subjects that are blamed for the high dropout rates. Implementing a theoretical framework into practice, we have been able to prevent 28% of our students from dropping out in the last 2 years due to our education reform. The aim of the present study is to analyse the results of the students through the curriculum of the CS program by factor analysis. Nearly 4000 first-year students’ results were analysed. One of the most important steps of the education reform was that all of the lectures became compulsory to attend. Another step was the introduction of a prevention and skills-training program for every first-year student in order to develop their study skills. Our findings highlighted that as a consequence of the education reform, more students stayed until the end of the first and second semesters and try taking exams in the exam period. Analysing the subjects as factors in the CS curriculum could (1) help faculty staff introduce an education reform, and (2) help decision-makers develop prevention and promotion programs in order to develop students’ study skills. The results reveal that we have managed to successfully engage first year students in the academic environments.
... Thus, CT encompasses a set of cognitive processes, such as decomposing problems into subproblems, abstracting, sequencing algorithms, control flow, debugging and generalizing. These processes enable learners to solve problems in a specific way, i.e., by creating algorithms (White and Sivitanides, 2003;Wing, 2006;National Research Council, 2010;Angeli et al., 2016). To plan a solution, a given problem needs to be decomposed into subproblems to reduce complexity. ...
Theory
Digital technologies have become an integral part of everyday life that children are exposed to. Therefore, it is important for children to acquire an understanding of these technologies early on by teaching them computational thinking (CT) as a part of STEM. However, primary school teachers are often reluctant to teach CT. Expectancy-value theory suggests that motivational components play an important role in teaching and learning. Thus, one hindrance to teachers’ willingness to teach CT might be their low expectancies of success and high emotional costs, e.g., anxiety towards CT. Thus, introducing preservice teachers to CT during their university years might be a promising way to support their expectancies and values, while simultaneously alleviating their emotional costs. Prior CT competences might contribute to these outcomes.
Aims
We investigated whether a specifically designed seminar on CT affected preservice teachers’ expectancies and values towards programming.Method: A total of 311 German primary school and special education preservice teachers took part in the study. The primary school preservice teachers received a seminar on CT and programming with low-threshold programming tasks, while the special education teachers served as a baseline group. The seminar was specifically designed to enhance expectancies and values and decrease emotional costs, following implications of research on expectancy-value theory.
Results
The preservice teachers who visited the seminar gained higher expectancies and values towards CT and programming compared to the baseline group. Moreover, their emotional costs decreased. CT was positively related to change in expectancies and values and negatively related to emotional costs.
Discussion
Interventions with low-threshold programming tasks can support primary school preservice teachers in finding trust in their abilities and values towards CT. Moreover, their anxiety towards CT and programming can be alleviated. Thus, first steps in preparing preservice teachers to teach CT in their future classrooms can be taken in university.
... A study by Mathews (2017) suggested that mathematical achievement would affect the success of programming learning. Another research confirmed that students' mathematic achievement is positively correlated with their levels in programming courses (White and Sivitanides, 2003). However, the predictive relationship between mathematic achievement and programming self-efficacy is unclear. ...
Purpose: This study aimed to explore the relationship between mathematic achievement and programming self-efficacy, and adopt a mediation model to verify the mediating role of creativity on the relationship between mathematic achievement and programming self-efficacy.
Methods: A total of 950 upper-secondary school students were surveyed using their math test scores, the Kirton Adaption-Innovation and the Programmed Self-Efficacy Scale. SPSS-26 was used for descriptive statistical analysis and correlation analysis of related variables. The PROCESS plugin was used to test the mediating effect of creativity.
Results: (1) Mathematic achievement has a positive effect on programming self-efficacy, mathematic achievement is positively related to creativity, and creativity also has a positive influence on programming self-efficacy. (2) Creativity has a mediating effect on the relationship between mathematic achievement and programming self-efficacy.
Conclusion: The results revealed that mathematic achievement affected programming self-efficacy directly and also indirectly through creativity. This provided certain ideas for the development of programming education for teenagers. Since students’ mathematics learning and creativity are related to programming learning, it is necessary to pay attention to the integration of the disciplines of programming education and mathematics. Further, the cultivation of innovative thinking is also critical to facilitate programming learning.
... To increase the success rate, the institutions must adopt novel procedures in these courses. A number of researchers, for instance [4][5][6], highlights the behaviour of students towards programming courses, the error they make and their response to the errors. Similarly, several researchers, for example [7], make use of applications to assess the problem solving skills of the students. ...
To preserve their reputation and prestige, the educational institutes are required to provide evidences of their students’ academic performance to the governmental bureaus and accreditation agencies. As a consequence, the monitoring individual student academic performance is emerging as a vital task for the educational institutes. The indispensability of this prediction amplifies when it comes to programming language course; which emerges as backbone for Computer Science students. Machine Learning classifiers are considered as productive tools to develop models which can identify the students with inefficient academic performance. The early identification of inefficient students will provide an opportunity to instructor to take appropriate precautionary measures. This paper proposes a prediction model with an added application layer with graphical user interface. The experimental part of paper compares the performance of several machine learning algorithms and comes up with k-NN as appropriate classifier in the addressed context. Further, the application layer of the proposed architecture facilitates instructor with a Graphical User Interface to execute a wide range of operations.
... One of the prime objectives of the existing studies is to identify the student's features which affect their learning [8]. For instance, several studies found that mathematical ability and exposure to mathematics courses are important predictors of the performance of introductory programming courses [9,10]. Numerous authors apply machine learning classifiers to automatically identify the students at risk of failures, for instance, Liao et al. [11], use support vector machines to automatically identify students at risk of failure based on data collected from instructors when they make use of the Peer Instruction, an active-learning methodology, pedagogy. ...
The new students struggle to understand the introductory programming courses, due to its intricate nature, which results in higher dropout and increased failure rates. Despite implementing productive methodologies, the instructor struggles to identify the students with distinctive levels of skills. The modern institutes are looking for technology-equipped practices to classify the students and prepare personalized consultation procedures for each class. This paper applies decision tree-based machine learning classifiers to develop a prediction model competent to forecast the outcome of the introductory programming students at an early stage of the semester. The model is then transformed into an adaptive consultation framework which generates three types of colored signals; red, yellow, and green which illustrates whether the student is performing low, average, or high respectively. This provides an opportunity for the instructor to set precautionary measures for low performing students and set complicated tasks that help the highly skilled students to improve their skills further. The experiments compare a set of decision tree-based classifiers and conclude J48 as an efficient model in classifying students in all classes with high accuracy, sensitivity, and F-measure. Even though the aim of the research is to focus on introductory programming courses, however, the framework is flexible and can be implemented in other courses.
... Over the years, failure in computer programming as well as difficulties experienced by students have been associated with so many factors. Our results were in consonance with some existing works. Mathematics was proven to predict success in procedural programming (Alspaugh, 1970;Ricardo, 1983;Ignatuk 1986). Also, Garry and Marcos. (2014), showed that a positive correlation exists between freshman Mathematics, SAT Mathematics score and Visual Basic programming course. Susan and Ronan (2005) also checked the relationship among mathematics, physics, biology, chemistry and programming performance and their research shows a positive correlation value with Mathematics having ...
... Devlin, 2001;Sidbury, 1986). Also a number of studies have been carried out to show the relationship between success in Mathematics and computer programming & algorithm courses (Group II) (e.g., White, 2003;White, & Sivitanides, 2003). ...
Aim/Purpose: The purpose of this study is to identify how Advanced level Mathematics and Mathematics course units offered at university level do impact on the academic performance of theoretical Computer Science course units. Background: In Sri Lankan state universities, students have been enrolled only from the Physical Science stream to do a degree program in Computer Science. In addition to that, universities have been offering some course units in Mathematics to provide the required mathematical maturity to Computer Science undergraduates. Despite of this it is observed that the failure rates in fundamental theoretical Computer Science course units are much higher than other course units offered in the general degree program every year. Methodology : Academic records comprised of all 459 undergraduates from three consecutive batches admitted to the degree program in Computer Science from a university were considered for this study. Contribution: This study helps academics in identifying suitable curricula for Mathematics course units to improve students’ performance in theoretical Computer Science courses. Findings: Advanced level Mathematics does not have any significant effect on the academic performance of theoretical Computer Science course units. Even though all Mathematics course units offered were significantly correlated with academic performance of every theoretical Computer Science course unit, only the Discrete Mathematics course unit highly impacted on the academic performance of all three theoretical Computer Science course units. Further this study indicates that the academic performance of female undergraduates is better than males in all theoretical Computer Science and Mathematics course units. Future Research: Identifying other critical success factors contributing to the students’ academic performance of the theoretical Computer Science through empirical studies
... It helps the students improve their problem-solving abilities as such studies confirmed that mathematics found to be one of the contributory factors on the success of the students taking information technology program (7,8) . Therefore, students must have a strong background in mathematics to have greater success in taking programming courses (9)(10)(11) . ...
Objectives: The aim of this study was to investigate the influence of mathematics to the programming performance of Information Technology students and identified the relationships of their performance in programming among genders. Methods/Statistical analysis: The study utilized the data mining method using J48 classification algorithm and descriptive-correlation design. The data were gathered from Electronic Database of the University. Failure ratings of the students were removed as significant outliers and came up with 73 data sets. Pearson r and Point Biserial Correlations were used with0.05 level of significance alpha to test the correlation between continuous measures of independent and dependent variables. Further, descriptive statistics were used to describe the level of performance in mathematics and programming. Findings: The results show that students demonstrated a high performance in their mathematics in the modern world course with a mean rating of 2.16 (SD=0.27), and a low performance in their mathematics enhancement 1 with a mean grade of 2.81 (SD=0.38). Similar result in their programming course with a mean grade of 2.64 (SD=0.39). The mathematics performance of the students is significantly correlated to their performance in programming. The low performance in mathematics enhancement 1corresponds to the low performance in programming. Moreover, female students performed better in their programming course compared to males. Applications: The results could help the teachers improve the quality of instructions particularly in mathematics and programming that will improve the performance of the students in both subjects. Concerned University administrators should conduct frequent assessments and curriculum revisits to examine possible areas of improvement beneficial to the students.
Keywords: Mathematics performance; programming performance; data mining; correlation; point biserial; J48 algorithm
... r programming (Galton, 1992;Gibbs & Tucker, 1986;Gomes & Mendes, 2007; G. L. White & Sivitanides, 2002). Past researches have shown that the development of higher cognitive abilities in terms of logical reasoning is important in understanding computer programming (Eckerdal, Thuné, & Berglund, 2005;Fletcher, 1984;Hudak & Anderson, 1990;Little, 1984;G. White & Sivitanides, 2003). The usage of programming controls, such as the selection www.mojet.net structure statement called the "if" statement, determine an action to be executed when a condition is met. This requires biconditional reasoning which is a precondition to formal operational reasoning (Gomes & Mendes, 2007;Ismail, Ngah, & Umar, 2010b;Lawson, 1983). ...
Difficulties in the learning of computer programming have been a universal problem. One of the main contributors of this problem is due to the teaching material used in guiding the students. Traditional teaching method using books and PowerPoints slides are not effective enough to support the dynamic nature of computer programming. Hence, a more effective teaching material such as screencasting is required to support the learning of computer programming. This study examined the moderating effect of logic in the learning of C++ computer programming using screencasting among undergraduates. A true experimental pre-test and post-test research design was conducted and the experiment involved 65 first-year undergraduate students (aged 19-22) who have never attended any formal computer programming course prior to the study. They were randomly assigned to two groups: the screencast and narration (SN) mode group and the screencast, text, and narration (STN) mode group. Results showed that the different levels of logic and the two treatment modes did not have any effect in the learning of C++ computer programming.
... Therefore, 5th-grade students were appropriate for this research. Since programming is related to problem-solving skills and there is a positive correlation between math and programming (White & Sivitanides, 2003), the administered pre-test was designed to test student problem-solving skills. Students were first exposed to Python programming for four weeks with two hours per week which makes a total of eight hours. ...
The link between problem-solving and programming skills is well known. Students with higher problem-solving abilities find programming easy and they can master programming with no or little difficulties regardless of the programming environment. On the contrary, students with lower problem-solving abilities find programming difficult to understand and are often unable to master it. The before mentioned groups of students usually make up two thirds of the entire class, the top and lowest thirds, respectively. What about the "middle third" students? This is probably the most represented group; those are students who can master programming but with some difficulties. Visual programming language environments are tools designed to engage all students but maybe the "middle third" students would gain the most benefit from that approach. In this paper, we explore the educational and motivational effect of using Scratch for game-based programming on 5th-grade elementary school students based on their problem-solving abilities. Results presented here confirm the positive effect of using Scratch as the introductory programming language for game-based programming on "middle third" students, compared to Python as the text-based programming language.
... In an effort to prevent students from performing poorly, many have turned to trying to determine what best predicts computing proficiency, presumably as a way to determine how to better teach students and/or determine which student may be more likely to need extra support during their first course. The research on success predictors goes back decades, with numerous factors being identified such as comfort level [45], math and/or science background [5,6,20,43,45], spatial visualization skills [35], attributions of success and/or confidence [6,34,45], learning style [9,35]. Watson [42] has argued that programming behavior was better predictor of success in the course than the traditional testbased prediction metrics and/or demographics. ...
A recent study about the effectiveness of subgoal labeling in an introductory computer science programming course both supported previous research and produced some puzzling results. In this study, we replicate the experiment with a different student population to determine if the results are repeatable. We also gave the experimental task to students in a follow-on course to explore if they had indeed mastered the programming concept. We found that the previous puzzling results were repeated. In addition, for the novice programmers, we found a statistically significant difference in performance based on whether the student had previous programming courses in high school. However, this performance difference disappears in a follow-on course after all students have taken an introductory computer science programming course. The results of this study have implications for how quickly students are evaluated for mastery of knowledge and how we group students in introductory programming courses.
... Since a good mark in Mathematics can possibly contribute to better performance in programming [1,4,5,7,19,33,34] and students whose first language differs from the teaching language may perform more poorly [6,9,19], it was expected that both the Mathematics and English marks of student might influence their performance. Prior programing experience has also been identified as contributing to performance in introductory programming courses [5,34]. ...
Introductory programming modules frequently have a low pass rate. This paper reports on an attempt to address this issue by introducing an interactive program visualization tutorial in the course material for an introductory programming module at the University of South-Africa (Unisa). The tutorial aimed to assist first-year programming students in an open, distance and e-learning (ODeL) environment in learning how to trace programs. To investigate the impact thereof students completed a questionnaire on their use of the tutorial, which were analysed in combination with their final marks for the module. This was repeated over two semesters.
The findings indicated that the tutorial did not have the desired effect in the ODeL environment. Students' educational background had a far larger impact on their final marks than their tutorial use, emphasising the real need to take educational background into account when developing course material, particularly in ODeL.
... Therefore those concepts cannot be compared. Results were analyzed by qualitative and quantitative techniques used for triangulation purpose [27] to increase the validity of the findings. ...
In the Republic of Croatia Informatics is an elective course in elementary school for students from 5th to 8th grade. When it comes to programming language teachers can choose between BASIC and Logo. There are a lot of new programming environments for learning and teaching programming like Scratch and also new ways of teaching programming like game based learning. This study compares attitudinal and learning outcomes of 7th-grade students programming in Logo and Scratch. The classes were normal classes, non-extracurricular activities. The questionnaire is used to measure the attitude towards programming and programming languages. The test is constructed to measure learned programming concepts in both compared programming languages Logo and Scratch. Results showed that learning Scratch first can provide a better understanding of basic programming concept for novices in elementary school than Logo.
... While solving problems on the computer, one of the most difficult tasks for the problem solver is writing the code. It's reported that programming requires high PS ability [19] but also that by programming PS ability can be practiced [9][2][10][11]. This is a little bit contradictory since if someone doesn't have high PS ability programming is hard and frustrating therefore will probably give up of it. ...
... struggle with learning to program. While some discussions on the cognitive aspects exist (Parnin 2010), typical approaches include programming aptitude tests (Evans andSimkin 1989, Tukiainen 2002), tests that measure skills such as mathematical and spatial reasoning (White andSivitanides 2003, Fincher et al. 2006), and the use of different self-perception metrics to detect students likely to succeed or fail (Bergin andReilly 2006, Ramalingam et al. 2004). With the improved computing facilities, more opportunities have arisen due to the ability to gather data from students' programming process, making it possible to capture and analyze programming sessions (Jadud 2006, Rodrigo et al. 2009, Watson et al. 2013. ...
Cognitive flexibility is an integral part in adaptive behavior such as learning, and can be seen as one explaining factor in performance in various tasks. In this study, we explore the possibilities of a dynamic psychological test that can be administered online to assess cognitive flexibility and apply the test across a student population that is learning to program. While our results imply that cognitive flexibility has little correlation with the score of a traditional pen-and-paper programming exam as well as the students' average grade from their first semester of studies, cognitive flexibility does play a role in the efficiency with which students solve programming errors. Moreover, our results imply that while cognitive flexibility correlates with the students' efficiency in solving programming errors, the correlation is more evident with novice programmers than with programmers with existing experience.
... However, it has been shown that women are rather than men to give up some more technical content of computer science like computer programming [13]. Excellent mathematical knowledge was confirmed as a strong predictor of success in programming through a number of studies [9,14,15]. Further exploration of mathematical knowledge as a predictor of success in programming has shown that good adoption of discrete mathematics and mathematical calculus could be considered a good predictor of success in programming [12]. ...
Despite the awareness of the important role of programming in today’s technological lifestyle and the fact that programming jobs are among the most sought after professions today, computer science students consider programming contents overly demanding and often avoid them.
This paper aims to provide an overview of the relevant literature and studies that examine factors that may significantly affect the performance of programming. We will give an overview of good and bad predictors of success in programming and factors that, so far, have not shown the prediction’s strength or their impact on the performance of programming hasn’t been sufficiently explored.
Knowledge of these factors is extremely important for computer science teachers in order to apply the appropriate teaching model for novices that emphasizes the development of the ability to anticipate and understand what happens when a specific computer program performs. Factors like good mathematical knowledge, attribution of success and good spatial maps sketching have proved to be good predictors of programming success while surface learning approach had shown strong negative impact on novices' programming performance. On the other hand, despite the expectations, student’s gender, number of years of programming, number of familiar programming languages haven't shown any significant impact on programming performance.
Keywords: Programming, novices, predictors, teaching.
Research Interests:
Programming Languages and Novice Programmers
... Since the 1950s, dozens of different factors have been investigated. This work included the creation of various psychological tests (Evans andSimkin 1989, Tukiainen andMönkkönen 2002) and investigation of the effect of factors such as mathematical background (White and Sivitanides 2003), spatial and visual reasoning (Fincher et al. 2006), motivation and comfort-level (Bergin and Reilly 2005), learning styles (Thomas et al. 2002), as well as the consistency of students' internal mental models (Bornat and Dehnadi 2008) and abstraction ability (Bennedssen and Caspersen, 2008). While many of these factors correlate with success within the study contexts, more recent research suggests that many of these might be context-specific, and urges researchers to look at data-driven approaches to investigate e.g., the students' ability to solve programming problems and errors from logs gathered from programming environments (Watson et al. 2014). ...
... There was contradictory literature on this question. Some suggested that computer programming did enhance cognitive skills such as reasoning, problem solving and logic thinking for high school and elementary school students (White & Sivitanides, 2003). At the same time, some other literature suggested that computer programming did not improve mathematical problem solving skills (Flores, 1985). ...
Information and communication technology (ICT) has increasingly been bringing about significant changes in education in an ongoing process. The educational reform is not a mere technological issue but rather is based on an empirical grounding in a psychological research approach to learning and instruction. This paper introduces the research work on the application of ICT in education from the psychological perspective in China in the past three decades. The introduction focuses on four important issues with systemic theoretical thinking based on continuous empirical research and innovative practices. The first is dialectic constructivism which has offered some dialectic explanation for knowledge, learning and teaching, and balanced various contradictory aspects of learning and teaching. The second is theoretical thinking and instructional practice about the principles of learning environment design which emphasises learners' higher-order thinking, deep understanding, collaboration and self-regulated learning. The third is a model for the effectiveness and conditions of Computer-Assisted Instruction. The fourth is a framework for the integration of ICT and education and a zigzag training model for teacher training for integration.
Programlama eğitiminde temel noktayı algoritma oluşturmaktadır. Ancak algoritma yazma sürecinde birtakım sorunlar yaşanmaktadır. Bu nedenle araştırmanın amacı bilgisayar mühendisliği öğrencilerinin algoritma sorularını çözerken yaşadıkları sorunları ortaya çıkarabilmektir. Araştırma bir üniversitenin bilgisayar mühendisliği programından 12 öğrencinin gönüllü katılımıyla gerçekleştirilmiştir. Araştırma bir durum çalışmasıdır. Bu bağlamda algoritma konusuyla ilgili biri metinsel biri akış diyagram içeren iki soru sorulmuştur. Araştırmada göz izleme yöntemiyle bilgisayar mühendisliği öğrencilerinin algoritma sorularını çözmeleri incelenmiş sonrasında algoritma dersi ve yapılan uygulama hakkındaki görüşleri görüşme formuyla alınmıştır. Araştırma verileri betimsel analiz teknikleriyle analiz edilmiştir. Göz izleme cihazından elde edilen sonuçlarda; öğrencilerin metinsel soruyu doğru cevaplama yüzdesi %25, ortalama çözme süresi 63.08 saniye, akış diyagramı içeren soruyu doğru cevaplama yüzdeleri %83,33, ortalama çözme süresi ise 41.25 saniye bulunmuştur. Araştırmanın görüşme sorularından elde edilen bulgularına göre öğrencilerin daha önce göz izleme cihazı kullanmadıkları (N=12), öğrencilerin algoritma dersine karşı ilgilerinin yüksek olduğu (N=12), algoritma sorularını çözerken en çok soruda istenilenlere odaklandıkları (N=12), algoritma sorularını sadece teorik anlatım olması yerine (N=5) uygulamalı (N=7) görmeyi tercih ettikleri, araştırma kapsamında sorulan soruların kolay olduğu (N=10), algoritma sorularını çoğunlukla açık uçlu soru (N=9) görmeyi tercih ettikleri, göz izleme yöntemiyle soru çözmenin onları çoğunlukla olumlu etkilediği (N=10) sonuçlarına ulaşılmıştır. Göz izleme cihazından elde edilen bulgular doğrultusunda öğrencilerin metinsel içerikli soruyu çözerken akış diyagramlı içeriğe sahip soruya göre daha fazla zaman ve çaba harcadıkları sonucuna ulaşılmıştır.
A felsőoktatási képzéseken megjelenő nagyarányú (általánosságban véve 30–40%-os) hallgatói lemorzsolódás súlyos problémákat okoz. Különösen érintett az informatikaoktatás, a legtöbb ország alapképzésének első két féléve a legkritikusabb: az intézmények elveszíthetik hallgatóik 60%-át. Vizsgálatunk célja annak feltárása, hogy a lemorzsolódás megelőzése érdekében bevezetett oktatási reform 2016 óta milyen mértékben befolyásolja a hallgatók egyetemen történő bennmaradását.
Programtervező informatikus hallgatók tanulmányi eredményeinek mintáját elemeztük 2010 és 2017 között az IRT-modell segítségével. A vizsgálatban 3673 egyetemi hallgató adatai szerepelnek.
Eredményeink szerint az oktatási reform után a legtöbb tantárgy teljesíthetőbbé vált, és az alacsonyabb képességű hallgatók is megpróbáltak vizsgát tenni. A matematikai témájú tantárgyak alacsonyabb nehézségi szinten is teljesíthetővé váltak, illetve alacsonyabb képességszintű hallgatók is vállalták a vizsgák megpróbálását, nem látták eleve kudarcnak a próbálkozást. A programozási/szakmai tárgyak nehezebbekké, komolyabb tárgyakká váltak, és megőrizték differenciálóképességüket. A hallgatók egyetemi előrehaladását segítő oktatási reform lehetővé tette a hallgatók tanulmányi teljesítményének javulását, ami arra enged következtetni, hogy érdemes beavatkozásokat eszközölni az egyes szakok képzési rendszerében.
Empirische Pädagogik ______________________ 35. Jahrgang 2021 1. Heft
Computer Science (CS) is a profession that positively impacts every single area of society without which today’s world would come to a complete halt. Yet, there is a consensus that CS has serious conundrums particularly in attracting students, low motivation for learning computer programming and developing computational thinking (CT) skills. New motivational methods are needed to take the attention of students and adapt to their learning patterns as how people learn have changed drastically over the last two decades. To address these issues, video games and video game-based tools are proposed as a primary approach for motivating and supporting students in developing their skills in CT and support their learning of introductory programming. This research is concerned with the capture of statistical evidence on the educational effectiveness of playing a serious game specifically designed to enhance the development of CT skills to facilitate learning introductory computer programming. A total of 190 students were invited to participate in a
quasi-experimental pre-post
study for the purpose of analyzing the impact of an
adhoc
game on students’ confidence in learning programming constructs and using their skills in CT. All students were studying a CS degree at the time and they were all registered to a first-year computer programming course. 151 out of 190 students successfully completed the study and the findings suggest that a) the intrinsic motivation to learn programming; b) students’ perception of their knowledge and their tangible knowledge in key programming constructs (i.e. programming sequence, methods, decision making and loops); and c) students’ confidence in using their CT skills were all statistically and significantly improved after playing the game. Additionally, students perceived computer programming significantly less difficult in their post study responses when compared to their pre study responses.
Este trabajo tiene como objetivo encontrar evidencia que respalde la hipótesis de que el razonamiento cuantitativo es un predictor relevante del rendimiento en cursos introductorios de matemática en carreras de Ciencia, Tecnología, Ingeniería y Matemática (STEM por sus siglas en inglés) controlando variables relevantes en la predicción de este rendimiento. El estudio se realizó en grupos de carreras, determinados por el curso de matemática introductoria compartido: a. Física, Meteorología y Química (n=132), b. Estadística (n=64) y c. Matemática y Ciencias Actuariales (n=87); con n igual a la cantidad de estudiantes por grupo. En cada agrupación se estimó un modelo de regresión lineal con medidas remediales apropiadas para el cumplimiento de los supuestos; la variable dependiente de los modelos fue la nota en el curso introductorio de matemática y las variables independientes fueron: el nivel de razonamiento cuantitativo, un indicador de las notas de secundaria y el examen de admisión, el sexo y la dependencia del tipo de colegio. En todos los modelos se obtuvo que la variable más relevante fue el nivel de razonamiento cuantitativo. Los coeficientes de determinación de los modelos fueron relevantes, ya que fueron superiores a 0.20.
Abstract—Teaching computer programming is recognized to
be difficult and a real challenge. The biggest problem faced by
novice programmers is their lack of understanding of basic
programming concepts. A visualized learning tool was
developed and used by volunteered first-year students for two
semesters. The purposes of this paper are: Firstly, to emphasize
factors which directly affect the performance of our students
negatively. Secondly, to examine whether the proposed tool
would improve their performance and learning progression or
not. This tool provides many features and enhancement which
were presented to students as pre-lecture material. The results
of adopting this tool were conducted using a pre-survey and
post-survey questionnaire. As a result, students who used the
learning tool showed better performance in their programming
In this paper, we investigate whether and how mathematics factors into students performance in IT learning. The involved cognitive levels of students learning mathematics and hence problem solving, are correlated to how well they are able to transpose their knowledge and apply it to problem solving in the IT field(s). Our hypothesis is that if students perform better in mathematics, in terms of level of mathematics course and grade earned in that course prior to engaging in IT studies, their performance in IT will also be higher. The performance of several groups of students, over a period of five semesters are collected, analyzed and the correlation between mathematics learning ability and that of IT is measured, analyzed, and the results are reported and therefore the hypothesis is tested.
Research over the past fifty years into predictors of programming performance has yielded little improvement in the identification of at-risk students. This is possibly because research to date is based upon using static tests, which fail to reflect changes in a student's learning progress over time. In this paper, the effectiveness of 38 traditional predictors of programming performance are compared to 12 new data-driven predictors, that are based upon analyzing directly logged data, describing the programming behavior of students. Whilst few strong correlations were found between the traditional predictors and performance, an abundance of strong significant correlations based upon programming behavior were found. A model based upon two of these metrics (Watwin score and percentage of lab time spent resolving errors) could explain 56.3% of the variance in coursework results. The implication of this study is that a student's programming behavior is one of the strongest indicators of their performance, and future work should continue to explore such predictors in different teaching contexts.
This review discusses recent literature on computer programming. It focuses on psychological studies of programming and selected issues related to instruction in programming. The purpose is to inform computer educators about the nature of the cognitive processes involved in programming, and the potential benefits to be gained from learning programming. Concerns relevant to pre-college students of programming are emphasized. The literature reviewed includes empirical studies of programming, rational analyses of the programming task by computer scientists, and expository essays by educators and psychologists. The literature is presented to illuminate three major issues: 1) what are the cognitive demands of programming and what are the possible cognitive outcomes? 2) how does instruction influence learning? 3) who benefits, and in what ways, from instruction?
The purpose of this study was to find out whether students perform differently on algebra word problems that have certain key context features and entail proportional reasoning, relative to their level of logical reasoning and their degree of field dependence/independence. Field-independent students tend to restructure and break stimuli into parts and to perceive details more readily than field-dependent students. The underlying theoretical view is that context may be an important factor in how students approach, analyze, and restructure word problems. The sample included university students (n=37) and secondary school students (n=193) from two large high schools in two cities. The Gottschaldt Hidden Figures Test was used to assess field dependence/independence. Selected items from the Equilibrium Balance Test were used to assess Piagetian stages of logical reasoning. A 2 x 3 MANOVA was used to analyze the effects of cognitive style (dependence, independence) and operativity (concrete, transitional, formal). Overall, field-independent subjects who were formal operational reasoners performed highest across all the problem features. The results supported the influence of cognitive style, together with cognitive development, in mediating a student's ability to solve algebra word problems. Contains 34 references and 2 test references. (Author/LDR)
R. B. McCall's (see record
1988-32308-001) article in this journal criticizing some of H. T. Epstein's (e.g., 1978) studies of brain and mind growth is shown to suffer from 2 main flaws. First, he was not aware of the need for analysis in terms of concurrences among studies rather than standard statistical analyses of individual studies. Second, he did not use increment spans appropriate for the data. He also wrongly claimed that Epstein advocated changes in educational policies and practices without caveats that carefully labeled these as suggestions for working hypotheses to be tested in the schools. Finally, his criticisms of the brain growth stages had already been invalidated in a 1986 article by Epstein which he did not cite. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This article reports on a year-long study of high school students learning computer programming. The study examined three issues: I) what is the impact of programming on particular mathematical and reasoning abilities?; 2) what cognitive skills or abilities best predict programming ability?; and 3) what do students actually understand about programming after two years of high school study? The results showed that even after two years of study, many students had only a rudimentary understanding of programming. Consequently, it was not surprising to also find that programming experience (as opposed to expertise) does not appear to transfer to other domains which share analogous forrnal properties. The article concludes that we need to more closely study the pedagogy of programming and liow expertise can be better attained before we prematurely go looking for significant and wide reaching transfer effects from programming.
In this article we describe the extent to which object-oriented programming (OOP) can be done in Visual Basic. We state the features generally considered necessary for a language to be object-oriented (OO) and give examples of how Visual Basic supports (or does not support) them. To try out these features, we started with a video game written in the pure OO language Eiffel and rewrote it in Visual Basic. The original design employed more than 70 new classes (i.e., classes not taken directly from a library), and made extensive use of inheritance, both single and multiple. Because inheritance is a feature that Visual Basic almost completely lacks, we were particularly interested in seeing how that part of the design would translate to the new language. For the most part, we were able to find straightforward means for converting the inheritance to client/supplier relationships. We discuss the tradeoffs involved in making this conversion.
This research investigated the cognitive characteristics for learning a visual programming language, Visual Basic. It replicated an earlier study by White (72) that used C++, an object-oriented programming language. The two cognitive characteristics investigated in this research with Visual Basic were 1) cognitive development, as measured by the Proposition Logic Test (PLT) and 2) cognitive hemispheric style, as measured by the Hemispheric Mode Indicator (HMI). Prior research has shown that object-oriented and procedural programming involves a high level of cognitive development and that procedural programmers are left brain hemispheric style thinkers. The findings from this study using a visual programming language, contradicts prior research with other programming paradigms. This study found visual programming being left cognitive hemispheric style, just like procedural programming. However, cognitive development was unimportant. While procedural and object-oriented languages require a high cognitive development level, visual programming languages require a lower level. This supports the theory that different programming language paradigms require different cognitive characteristics (73).
A routine EEG combined with alpha-index and spatial synchronization (SpS) analysis was performed in two different populations, native citizens of the Far North-East of Russia and new immigrants to the Far North-East. The investigation was performed in the initial resting state, in the process of mental arithmetic and of imagination of a natural landscape. According to the significant increase in spatial synchronization during task solution, all subjects were divided into 3 groups: I. Mental imagination increased SpS in the right hemisphere (> .7) while mental arithmetic did not increase SpS in either hemisphere. - II. Mental arithmetic increased SpS in the left hemisphere (.7) while mental imagination did not increase SpS in either hemisphere. - III. Mental imagination increased SpS in the right hemisphere while mental arithmetic increased SpS in the left hemisphere. In group I, alpha-index was relatively reduced in the left hemisphere before task performance and in both hemispheres during mental arithmetic, while mental imagination was accompanied by high alpha-index. In group II both tasks, and especially imagination, caused a decrease in the alpha-index. Native citizens and children under age 10 more often belonged to the group I and adult new immigrants - to the group II. The nature of the brain hemisphere activation is discussed in terms of the peculiarity of brain hemisphere functions - organization of the poly - monosemantic context.
The purposes of this study were (a) to determine if learning to program a computer in either BASIC or Logo improves the problem-solving skills of fifth grade students when compared to a control group that receives no programming instruction, and (b) to determine if learning to program a computer in Logo is more effective than learning to program in BASIC. Subjects were 132 fifth graders from two suburban elementary schools. Subjects in the BASIC treatment group received seven weeks (approximately 35 hours) of computer programming instruction using the Computer Challenge Guide. Subjects in the Logo group received the same amount of instruction using Logo in the Classroom. The New Jersey Test of Reasoning Skills was used as the pretest and posttest measure, and data were analyzed using the analysis of covariance procedure. Results indicated that learning to program in BASIC or Logo does not significantly improve problem-solving abilities of fifth grade students, as measured by the instrument used, when compared to a group that receives no programming instruction.
The purpose of this study was to investigate the relationships among cognitive development (as defined by Piaget), selected programming aptitudes, and performance in a BASIC programming class by children and adolescents. Cognitive development, verbal reasoning, and quantitative reasoning were examined as they relate to performance in BASIC programming.Fifty-seven children and adolescents attending public schools in Broward County, Florida served as subjects for the study. Ages of the subjects ranged from eight to fourteen years. The students voluntarily enrolled in a 30 hour class in BASIC programming. Four tests were administered to each subject: the Inventory of Piaget's Developmental Tasks (IPDT), designed to measure Piagetian development, and the Verbal Reasoning, Number Series, and Quantitative Relations subtests of the Cognitive Abilities Test as measures of verbal and quantitative reasoning. The measures of Piagetian development, verbal reasoning, and quantitative reasoning comprised the independent variables. The dependent variables were sums of scores on tests of interpreting and generating BASIC programs; these tests were administered at the end of each unit of study. A multiple regression model was utilized to analyze the data.The results indicated that cognitive development, as measured by the IPDT, was strongly related both to interpreting and generating BASIC programs. When IPDT was entered into regression equations with the other variables (verbal reasoning, quantitative reasoning, and age), IPDT was the only significant predictor of both interpretation and generation scores. When IPDT was not included in the analysis, quantitative reasoning was significantly related to both interpretation and generation tasks; age was a significant predictor of interpretation scores, but did not significantly predict generation scores. Verbal reasoning did not significantly add to the prediction of performance in this sample of children and adolescents when combined with the other variables of level of development, quantitative reasoning, and age.
Sixty first-year college student in a state university were administered three problems to ascertain their level of logical thinking. Successful solution of the problems required the elaboration of hypotheses and methods of proof. Seventeen percent of the randomly selected Ss scored at the concrete level, 63% at the lower formal level, and 20% at the upper formal level. Logical thinking was found to have almost no relationship to college selection criteria (high school rank and SAT scores) for men and a low relationship for women in this sample whose range of scores on these criteria was restricted. Differences between the sexes were found to be substantial and significant, favoring the men.
Measures of formal operations and learning style predicted success in introductory statistics and computer science courses. Using a final course grade cutoff of 80% or better as a criterion of success in the course, a discriminant analysis correctly classified 81% of the statistics students and 72% of the computer science students. Success was related to the presence of the ability to act as a formal operator and to the absence of a reliance on the concrete experiences learning style. These findings highlight the need to examine both cognitive maturity and learning style in studies of academic success at the college level.
This study examined the relationship between cognitive characteristics (field-independence, spatial visualization, logical reasoning, and direction following) and the initial acquisition of computer programing competence in 29 computer programing students. Students completed tests and surveys; results suggested that individual differences be considered in programming instruction regardless of programing language and age. (SM)
To better determine the cognitive abilities needed for computer programming, a study was done using 41 junior and senior level college students majoring in social and behavioral sciences at a Los Angeles area university. The subjects were first tested to determine their cognitive abilities with regard to spatial ability, field dependence/independence, general reasoning or non-verbal intellect, analytic versus holistic processing, and math skills. These results were then compared with the results of a test of the subjects' computer programming skills after 10 weeks of computer instruction. Results of this comparison show a positive correlation between computer programming and several cognitive abilities. A comparison of the results of this study with those of a similar study done by Shaha (1983) using graduate students indicates that this study verifies Shaha's findings with one major exception--the order of regression coefficients for overall programming ability. A table of regression coefficients and a bibliography are attached. (Author/JB)
The subjects for this study, which examined the relationship of cognitive style and ability to the initial acquisition of computer programming competence, were 46 college students enrolled in a computer literacy course. The selected cognitive style (field-independence) and three cognitive abilities (logical reasoning, spatial ability, and direction following) were correlated to five programming component skills: (1) knowledge of BASIC syntax; (2) ability to predict program outcome; (3) ability to design and write programs; (4) ability to debug programs; and (5) ability to modify programs in BASIC. It was found that field-independence, logical reasoning, and direction following were strongly related to most programming skills, while spatial ability was related only to debugging and modification. The text is supplemented by one figure. (26 references) (Author/EW)
Taking the current excitement among educators concerning the uses of microcomputers for student instruction as a point of departure, this paper addresses the problems and possibilities associated with the uses of microcomputers in the classroom and discusses these in terms of instructional techniques and the social aspects of integrating computer activities into classroom environments. A pilot program in which microcomputers were used to teach programming in elementary school classes is described and the six major instruction-related uses of computers--drill-and-practice, tutorial computer-assisted instruction, computer-managed instruction, simulation and model building, the development of computerized information skills, and teaching computer programming--are carefully reviewed. Special attention is given to the place of computer programming in the school curriculum and the advantages and disadvantages of the BASIC programming language are outlined. The social organization of computer use in schools is then considered. A summary of recommendations for researchers, developers of computer-based educational materials, and school system administrators concludes the paper. A 45-item reference list is attached. (Author/JL)
Presents the results of two Piagetian conservation tasks administered to high school students. It was found that not many were at the formal operations level. Similar evidence is presented for college students. Concludes that development of formal thought must be a focus of all teachers. (DF)
Industry and Academia are moving from procedural programming languages
(e.g., COBOL) to object-oriented programming languages, such as Java for
the Internet. Past studies in the cognitive aspects of programming have
focused primarily on procedural programming languages. Some of the
languages used have been Pascal, C, Basic, FORTAN, and COBOL.
Object-oriented programming (OOP) represents a new paradigm for
computing. Industry is finding that programmers are having difficulty
shifting to this new programming paradigm. This instruction in OOP is
currently starting in colleges and universities across the country. What
are the cognitive aspects for this new OOP language Java? When is a
student developmentally ready to handle the cognitive characteristics of
the OOP language Java? Which cognitive teaching style is best for this
OOP language Java? Questions such as the aforementioned are the focus of
this research Such research is needed to improve understanding of the
learning process and identify students' difficulties with OOP methods.
This can enhance academic teaching and industry training (Scholtz, 1993;
Sheetz, 1997; Rosson, 1990). Cognitive development as measured by the
Propositional Logic Test, cognitive style as measured by the Hemispheric
Mode Indicator, and physical hemispheric dominance as measured by a
self-report survey were obtained from thirty-six university students
studying Java programming. Findings reveal that physical hemispheric
dominance is unrelated to cognitive and programming language variables.
However, both procedural and object oriented programming require
Piaget's formal operation cognitive level as indicated by the
Propositional Logic Test. This is consistent with prior research A new
finding is that object oriented programming also requires formal
operation cognitive level. Another new finding is that object oriented
programming appears to be unrelated to hemispheric cognitive style as
indicated by the Hemispheric Mode Indicator (HMI). This research
suggests that object oriented programming is hemispheric thinking style
friendly, while procedural programming is left hemispheric cognitive
style. The conclusion is that cognitive characteristics are not the
cause for the difficulty in shifting from procedural to this new
programming paradigm of object oriented programming. An alternative
possibility to the difficulty is proactive interference. Prior learning
of procedural programming makes it harder to learning object oriented
programming. Further research is needed to determine if proactive
interference is the cause for the difficulty in shifting from procedural
programming to object oriented programming.
"Once upon a time, in the kingdom of Batch, all access to the magical shrine Mainframe was controlled by a guild of wizards called Assemblers. They offered the machine-punched cards with the arcane symbols of their calling. Then, the monks of the benevolent orders Fortran and Cobol settled in the kingdom, and those who learned their language could actually understand what was in columns 7 through 72 of the sacrificial cards. But eventually the knights of Dartmouth, Sirs Kemeny and Kurtz, revolted against the evil card queue and let ordinary citizens submit Basic petitions to the gods through public shrines called Terminals..." The early history of computers sometimes does seem like a fairy tale to those who did not have to grapple with its foibles. The appearance of the Basic language (and the similar Focal) on early DEC machines in the 1960s did make computer programming available to ordinary folks, so to speak. In the next decade, Li Chen Wang's Tiny Basic and Microsoft's Basic interpreter for the Altair set the stage for the microcomputer revolution. For some time, Microsoft's Quick Basic was the only programming language available for PCs.
Growing out of a child's cognitive developmental history, formal operations become established at about the age of 12-15 years. Reflected in his ability to reason hypothetically and independently on concrete states of affairs, these structures may be represented by reference to combinatorial systems and to 4-groups. The essence of the logic of cultured adults and the basis for elementary scientific thought are thereby provided. The rate at which a child progresses through the developmental succession may vary, especially from one culture to another. Different children also vary in terms of the areas of functioning to which they apply formal operations, according to their aptitudes and their professional specializations. Thus, although formal operations are logically independent of the reality content to which they are applied, it is best to test the young person in a field which is relevant to his career and interests.
To test the hypothesis that the basic “logic” utilized by individuals in scientific hypothesis testing is the biconditional (if and only if), and that the biconditional is a precondition for the development of formal operations, a sample of 387 students in grades eight, ten, twelve, and college were administered eight reasoning items. Five of the items involved the formal operational schemata of probability, proportions and correlations. Two of the items involved propositions and correlations. Two of the items involved propositional logic. One item involved the biconditional. Percentages of correct responses on most of the items increased with age. A principal-component analysis revealed three factors, two of which were identified as involving operational thought, one of which involved propositional logic. As predicted, the biconditional reasoning item loaded on one of the operational thought factors. A Guttman scale analysis of the items failed to reveal a unidimensional scale, yet the biconditional reasoning item ordered first supporting the hypothesis that it is a precondition for formal operational reasoning. Implications for teaching science students how to test hypotheses are discussed.
A large proportion of science major college students are unable to translate even simple sentences into algebraic equations. Given the following sentence: There are six times as many students (S) as professors (P) at this university, most students write the following equation: 6S = P, referred to as the reversal error. In order to overcome the reversal error students need to operate in a hypothetico-deductive manner, i.e., performing a hypothetical operation that makes the group of professors six times larger than it really is (S = 6P). The objective of this study is to investigate the relation between student ability to translate sentences into equations, equations into sentences, and student performance in the following variables: formal operational reasoning, proportional reasoning, and introductory freshmen-level chemistry course. The results obtained show that as the student ability to translate sentences into equations and equations into sentences increases, their mean scores in Chemistry I, formal operational, and proportional reasoning increases. This study has found support for the hypothesis that students who lack formal operational reasoning skills (hypothetico-deductive reasoning) may experience more problems in the translation of algebraic equations.
development have been amassed to the point that they may be used to understand some significant aspects of the biological origins of thought and language. Such biological correlates provide guidelines for and limitations on the kinds of functioning compatible with the biological information. The purpose of these remarks is to set forth what seem to me to be some relevant biological information about brains, and to indicate how it may relate to those functions and, especially, to children's development of thought, language, and reading. In so doing, as is true of all projections, some steps will be taken beyond the safe confines of direct proof; the reader is asked to accept such steps as they are intended: suggestions that may prove useful for guiding new experiments which may lead to new and expanded understanding.
First-year computer science students need to know and use a considerable amount of mathematics. A corequired course in discrete mathematics is a good solution.
Typescript (photocopy). Thesis (Ph. D.)--Georgia State University, 1983. Includes bibliographical references (leaves 70-75).
Thesis (Ph. D.)--United States International University, San Diego Campus, 1986. Includes bibliographical references (leaves 222-238).
This study investigated the relationship between the student's grade in a beginning computer science course and their sex, age, high school and college academic performance, number of mathematics courses, and work experience. Standard measures of cognitive development, cognitive style, and personality factors were also given to 58 students in three sections of the beginning Pascal programming class. Significant relationships were found between the letter grade and the students' college grades, the number of hours worked and the number of high school mathematics classes. Both the Group Embedded Figures Test (GEFT) and the measure of Piagetian intellectual development stages were also significantly correlated with grade in the course. There was no relationship between grade and the personality type, as measured by the Myers-Briggs Type Indicator (MBTI); however, an interesting and distinctive personality profile was evident.
Thesis (Ph. D.)--University of Texas at Austin, 1997. Vita. Includes bibliographical references (leaves 140-150).
Thesis (Ph. D.)--Syracuse University, 1972. Includes bibliographical references (leaves 355-358). Vita. Microfilm. s
Thesis (Ph. D.)--University of Minnesota, 1999. Includes bibliographical references (leaves 81-84).
Thesis (Ed. D.)--University of Georgia, 1984. Directed by Michael J. Padilla. Includes bibliographical references (leaves 113-119).
Contenido: Aplicación de la psicología a la enseñanza; Estadios del desarrollo psicosocial y cognoscitivo; Características de los niveles de edad; Teorías de la conducta; Procesamiento de Información; Estrategias cognoscitivos; Objetivos; Motivación; Evaluación; Interpretación de Resultados y Pruebas estandarizadas; Niños excepcionales; Control en el aula.
A nine-step method for overcoming the limitations of traditional
models when designing visual applications is presented. Microsoft Visual
Basic is used to demonstrate the technique that can be applied to most
visual programming languages. The method described takes account of
factors such as an interface driven approach, the psychology of
programmer commitment, the need to develop readable code, and provides a
method of modularly designing detailed test documents. The method has
proved to be suitable for existing Visual Basic developers and those
wishing to move from procedural into visual programming
A Study of the Relationships between Student Characteristics and Proficiency in Symbolic and Algebraic Computer Programming
- C A Alspaugh
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between Student Characteristics and Proficiency in
Symbolic and Algebraic Computer Programming."
Dissertation Abstracts International, 31(4627B).
A Predictor for Success in an Introductory Programming Class based upon Abstract Reasoning Development
- R J Barker
- E A Unger
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Success in an Introductory Programming Class based
upon Abstract Reasoning Development." Proceedings
of the 14 th SIGCSE Technical Symposium on
Computer Science Education of the ACM, Orlando,
Florida.
Are I.S.U. Freshman Students Operating at a Formal level of thought Processes?
- S Bastian
- J Frees
- L Gruber
- J Johnson
- B Landes
- L Morton
- S Rozgony
- J Stewart
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Morton, L., Rozgony, S., and Stewart, J. (1973). "Are
I.S.U. Freshman Students Operating at a Formal level
of thought Processes?" Contemporary Education, 44,
358-362.
Visual tools: Pros and cons
- M Buchner
Buchner, M. (1999). "Visual tools: Pros and cons."
Midrange Systems, 12(6), 18.
The Relationship of Piagetian formal Operations and other cognitive factors to computer programming ability (Development)
- R Cafolla
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Operations and other cognitive factors to computer
programming ability (Development)." Dissertations
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Effects of Computer Programming on the Learning of Calculus Concepts
- A Flores
Flores, A (1985). "Effects of Computer Programming on
the Learning of Calculus Concepts." Dissertation
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A Complete Trilogy of Visual Basic tools: Code Complete from MicroHelp
- R Grehan
Grehan, R. (1996a). "A Complete Trilogy of Visual Basic
tools: Code Complete from MicroHelp." Byte, 21, 32.
Visual programming for Science: Visual Science from acroScience
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Grehan, R. (1996b). "Visual programming for Science:
Visual Science from acroScience." Byte, 21, 208.
The Study of the Cognitive Development of Science Students in Introductory Level Courses
- D H Griffiths
Griffiths, D. H. (1973). "The Study of the Cognitive
Development of Science Students in Introductory
Level Courses." ERIC (ED096108).
Delphi and Visual Basic
- J Holtzman
Holtzman, J. (1996). "Delphi and Visual Basic."
Electronics Now, 67(10), 66-68.
An Analysis of the Effects of Computer Programming on Analytical and Mathematical skills of high school students
- N Ignatuk
Ignatuk, N. (1986). "An Analysis of the Effects of
Computer Programming on Analytical and
Mathematical skills of high school students."
Dissertation Abstracts, A47(3), 854.
The Reasoning Ability and Achievement of College Level Students enrolled in a Logic Class in Computer Science
- Y Kim
Kim, Y. (1995). "The Reasoning Ability and Achievement
of College Level Students enrolled in a Logic Class in
Computer Science." Unpublished Dissertation,
University of Texas, Austin.