Data Structures Course Research Articles

Exploration and Practice of IPTTAC——Taking the Course of Data Structure as an Example

Talent training in Colleges and universities is a process of the unity of education and talent cultivation. To build a high-level talent training system, we must consider the ideological and political work system, pay attention to the construction of Ideological and Political Theories Teaching in All Courses(IPTTAC), and solve the problem of "two skins" of professional education and ideological and political education[1]. In the teaching process of data structure course, Teachers practice IPTTAC and explore a series of methods of IPTTAC. Breaking the boundaries of disciplines, carrying out benign interaction, engineering culture and IPTTAC going hand in hand, engineering ethics education, socialist core values and Chinese traditional culture should be applied to IPTTAC.Paying attention to teachers' ethics and style, teaching by words and deeds, it will prompt IPTTAC. In teaching, we should start from the professional curriculum itself, explore the content related to ideology and politics, extend and expand the curriculum content, and enrich the charm of the curriculum itself.

The Grading Model of College Minority Students Based on Visual Information Technology

At present, the most effective teaching tool is the interactive visualization platform, which directly or indirectly promotes the development of the education field. In addition, the development of this technology will affect many existing fields. The purpose of visual education is to help learners grasp relevant concepts in teaching more clearly. At the same time, visualization technology has a wide range of applications in various teaching fields of computer technology. In programming and data structure courses, the use of visualization teaching platform can deepen the understanding of learners. Based on the visualization information technology, this paper makes an in-depth study on the grading model of minority students in colleges and universities. This paper designs a scoring model by using visualization technology, and uses the model to collect teachers’ scoring results of various indicators of ethnic minority students in colleges and universities, and finally analyses the data.

The Role of Self-Regulation in Remote Emergency Learning: Comparing Synchronous and Asynchronous Online Learning

Due to the COVID-19 pandemic, schools and universities across the world have had to switch to online learning, which is offered either synchronously or asynchronously. This study examined the role of self-regulation on students’ performance in each of these modes by comparing the use of self-regulation skills between high and low achievers in each mode and assessing the relationships of using these skills with students’ performance. The data were collected from students who enrolled in a data structures course in fall 2020 in either synchronous or asynchronous mode. The results show that self-regulation is an essential factor for learners’ success in both modes of online learning. However, there was a variance of using self-regulating learning strategies between students in synchronous and asynchronous modes.

Fostering Innovation and Methodology through Experiential Learning for Data Structures

For real-life work environment, developing employability skills by the students’ community is indispensable and need of the hour. Laboratories associated with courses cultivate the ability of students to think independently and promote employability skills through experiential learning. Data structures are one such predominant course in IT/CSE domain to enhance the skill set of students for Placements/Higher studies. It provides opportunity for students to devise algorithms efficiently in an optimal manner by solving challenging problems easily thereby gaining confidence for attending placements. The purpose of this paper is to foster innovation by exploration in data structures course through experiential learning by providing coding based challenging questions as tutorials, assignment presentations and as activities during laboratory sessions and online classes are to be conducted as an essential one in the future learning perspective. Findings conclude that the experiential learning foster student’s abilities in solving the problem and thereby gaining confidence during e-learning and placement activities and the grading process are to be updated through centralized servers to the maximum extend and accuracy are to be maintained.

Initial Suspicion on Detecting Code Plagiarism and Collusion in Academia: Case Study of Algorithm and Data Structure Courses

In engineering education, some assessments require the students to submit program code, and since that code might be a result of plagiarism or collusion, a similarity detection tool is often used to filter excessively similar programs. To improve the scalability of such a tool, it is suggested to initially suspect some programs and only compare those programs to others (instead of exhaustively compare all programs one another). This paper compares the ef-fectiveness of two common techniques to raise such initial suspicion: focusing on the submissions of smart students (as they are likely to be copied), or the submissions of slow-paced students (since those students are likely to breach academic integrity to get higher assessment mark). Our study shows that the latter statistically outperforms the former by 13% in terms of precision; slow-paced students are likely to be the perpetrators, but they fail to get the submissions of smart students.

COMPREHENSION OF LINKED LIST DIAGRAMS: THE EFFECTS ON CODE WRITING SKILLS

The use of graphics has a tendency to aid reasoning in program solving by improving novice programmers’ ability to read and write code. This study extends existing work in computer programming on the use of diagrammatic representation for students undertaking the fundamental data structure course (CS2) in Malaysia. Students were tested on comprehension of diagrams followed by the composition of code with respect to the linked list topic. The data was assessed using the inter-rater agreement test and showed a high degree of consistent ratings. Results showed a moderate correlation between students’ ability to analyze list operations in the form of notation and performance on code writing. Students assessed the diagrams differently according to the complexity level. The result can be generalized to conclude that the use of diagrams alone may not fully support reasoning and program solving. However, some types of diagrams are potentially more effective to support code composition and more emphasis should be given to evaluating the effectiveness of diagrams in organizing cues to facilitate novice programmers in program solving. Further investigation on a combination of activities related to comprehension of diagrams, including code reading and explanation prior to code writing, is recommended.

Research and Practice of Data Structure Curriculum Reform Based on Outcome-Based Education and Chaoxing Platform

In view of the problem of the data structure course being difficult to learn and teach, this paper analyses the characteristics and pain points of the course. We implement the concept of 'student-centered' education, combine outcome-based education and heuristic teaching, take students' learning effect as guidance and construct the curriculum according to the requirement of the industry. Combining students' autonomous learning, teachers' blackboard writing and on-site programming, we practice the educational concept of students as the main body and teachers as the auxiliary part. Finally, we highlight the significance and prospects of the results of this research and conclude that the research results are effective.

Prediction of Students’ Academic Performance Based on Courses’ Grades Using Deep Neural Networks

Predicting students’ academic performance at an early stage of a semester is one of the most crucial research topics in the field of Educational Data Mining (EDM). Students are facing various difficulties in courses like “Programming” and “Data Structures” through undergraduate programs, which is why failure and dropout rates in these courses are high. Therefore, EDM is used to analyze students’ data gathered from various educational settings to predict students’ academic performance, which would help them to achieve better results in their future courses. The main goal of this paper is to explore the efficiency of deep learning in the field of EDM, especially in predicting students’ academic performance, to identify students at risk of failure. A dataset collected from a public 4-year university was used in this study to develop predictive models to predict students’ academic performance of upcoming courses given their grades in the previous courses of the first academic year using a deep neural network (DNN), decision tree, random forest, gradient boosting, logistic regression, support vector classifier, and K-nearest neighbor. In addition, we made a comparison between various resampling methods to solve the imbalanced dataset problem, such as SMOTE, ADASYN, ROS, and SMOTE-ENN. From the experimental results, it is observed that the proposed DNN model can predict students’ performance in a data structure course and can also identify students at risk of failure at an early stage of a semester with an accuracy of 89%, which is higher than models like decision tree, logistic regression, support vector classifier, and K-nearest neighbor.

Fostering Innovation and Methodology through Experiential Learning for Data Structures

For real-life work environment, developing employability skills by the students’ community is indispensable and need of the hour. Laboratories associated with courses cultivate the ability of students to think independently and promote employability skills through experiential learning. Data structures are one such predominant course in IT/CSE domain to enhance the skill set of students for Placements/Higher studies. It provides opportunity for students to devise algorithms efficiently in an optimal manner by solving challenging problems easily thereby gaining confidence for attending placements. The purpose of this paper is to foster innovation by exploration in data structures course through experiential learning by providing coding based challenging questions as tutorials, assignment presentations and as activities during laboratory sessions and online classes are to be conducted as an essential one in the future learning perspective. Findings conclude that the experiential learning foster student’s abilities in solving the problem and thereby gaining confidence during e-learning and placement activities and the grading process are to be updated through centralized servers to the maximum extend and accuracy are to be maintained.

The Impact of Pair Programming on the Performance of Slow-Paced Students

A study shows that pair programming can help slow-paced students in completing Introductory Programming assessment. This paper replicates the study on Data Structure course, in which the completion of the assessments does not only rely on logic but also theoretical knowledge. The aim is to check whether pair programming is still helpful on such new assessment characteristics. Three classes of Data Structure course with 14 teaching weeks and a total of 72 undergraduate students are considered in this study. Two of the classes are about Basic Data Structure while another one is the advanced one. Our evaluation shows that pair programming can help slow-paced students in both pair and individual academic performance. It also increases overall academic performance if the tasks are more logic oriented. Nevertheless, no benefits provided for fast-paced students paired to the slow-paced ones, even though all students appreciate the use of pair programming.

An Analysis on the Teaching Reform of Data Structure Course in China

For the course of "Data Structures", this paper introduces the importance and existing problems of the data structure course. Through the literature and the current teaching in major universities, the existing teaching methods and their disadvantages are analyzed. The authors put forward teaching reform suggestions and designed an online course platform.

Self-regulated learning with massive open online course (MOOC) for the fundamentals of data structure course: A descriptive analysis

Self-regulated learning with massive open online course (MOOC) for the fundamentals of data structure course: A descriptive analysis

A new local causal learning algorithm applied in learning analytics

PurposeThe paper aims to present a new two stage local causal learning algorithm – HEISA. In the first stage, the algorithm discoveries the subset of features that better explains a target variable. During the second stage, computes the causal effect, using partial correlation, of each feature of the selected subset. Using this new algorithm, the study aims to identify the actions that lead a student succeed or failure in a course.Design/methodology/approachThe paper presents a brief review of main concepts used in this study: Causal Learning and Causal effects. The paper also discusses the results of applying the algorithm in education data set. Data used in this study was extracted from the log of actions of a Learning Management System, Moodle. These actions represent the behavior of 229 engineering students that take Algorithm and Data Structure course offered in a blended model.FindingsThe algorithm proposed in the paper identifies that features with weak relevance to a target may become relevant when computing the direct effect.Research limitations/implicationsThe algorithm needs to be improved to automatically discard attributes that are under a specific threshold of direct effect. Researchers are also encouraged to test the proposed propositions further.Practical implicationsThe algorithm presented in this paper can be used to identify the mostly relevant features given a classification task.Originality/valueThis paper computes the direct effect of a selected subset of features in a target variable to evaluate if a variable in this subset is really a cause of the target or if it is a spurious correlation.

Increasing the Engagement Level in Algorithms and Data Structures Course by Driving Algorithm Visualizations

The paper presents the results of our research in the field of applying algorithm visualizations within Data structures and algorithms subject. We accomplished several experiments relating the ability of students to solve simple problems in a pure visual way in one case and by programming the solution using a particular programming language in another one. The experiments are described and the results are analyzed within the paper. In accordance with our previous informal experiences and the results of the analysis we found there can be some part of students, which will be able to apply an algorithm to concrete problem in a visual way, but will not be able to express it clearly enough and implement it in given programming language. As an attempt to cope with the situation, we propose a new teaching approach, together with a prototype of study supporting system, based on the idea that students would participate on creating simple visualizations, not just using them. The purpose of such approach is to help students to develop both types of skills - understanding the algorithms and implementing them as well, by increasing the engagement level and supporting the active learning.

Analysis of Fuzzy Logic Modification for Student Assessment in e-Learning

The phenomenon of the rapid transfer of learning to online systems, such as e-Learning, has occurred massively. Institutions must ensure that student assessments run well. The characteristics of learning in e-Learning require an appropriate assessment method. The fuzzy logic method can be an option. Research shows that fuzzy logic is capable of providing flexible and objective performance evaluation. Fuzzy logic is a method that can overcome the uncertainty of transparency and objectivity of student assessments. In general, fuzzy logic applications are carried out by standards. Modification is an attempt to reveal the flexibility and to optimize the use of fuzzy logic. This study presents an analysis of fuzzy logic modification for the assessment of Algorithm and Data Structures courses held in e-Learning. These modifications include (i) modification of the parameter score with score compatibility, (ii) consequent modification of the fuzzy rules and (iii) modification of the implication process. The study results show that although the use of fuzzy logic requires more complicated procedures and tools, it can present various kinds of assessment as an option for educators to assess students in e-Learning.

The Algorithms and Data Structures course multicomponent complexity and interdisciplinary connections

The Algorithms and Data Structures course multicomponent complexity and interdisciplinary connections

Exploration and Consideration on Teaching Reform of Data Structure Course for the Engineering Education Professional Certification

As a core curriculum of computer science, data structure plays a key role in connecting the preceding with the following in the professional curriculum system. This paper analyses the problems existing in the traditional teaching method of data structure course. Based on the results-oriented concept of engineering education for professional certification, the student-centered and continuous improvement concept of teaching, some ideas and measures for the reform of data structure course are presented, including adjusting teaching aims and improving teaching methods and reforming assessment methods, etc. Through the reform, we can effectively improve the teaching effect of data structure course, learning interest and initiative of the student and improve engineering practice ability for them.

THE EFFECTIVENESS OF THE USE OF DIGITAL TECHNOLOGY IN THE PROCESSING OF POSTAL ITEMS IN AUTOMATED TECHNOLOGICAL PROCESSES

This article introduces students to how to succeed in a data structure and how to teach it in a broad way. Interesting assignments encourage students to actively learn from the data structure. Encourages them to actively engage in science - related material. It is important for active students to take an introductory course in data structure, where students will also learn the basics of programming. In doing so, the author evaluates their programs during a task by broadly assessing student learning by allowing students to compete in programming based on the idea of competitive programming in describing a project for a data structure course, and to improve their code by instructors. Develops an excellent program result by comparing it with other student code in the tournament environment by fighting codes.

DATA STRUCTURE AND THE NEED FOR TEACHING

This article introduces students to how to succeed in a data structure and how to teach it in a broad way. Interesting assignments encourage students to actively learn from the data structure. Encourages them to actively engage in science - related material. It is important for active students to take an introductory course in data structure, where students will also learn the basics of programming. In doing so, the author evaluates their programs during a task by broadly assessing student learning by allowing students to compete in programming based on the idea of competitive programming in describing a project for a data structure course, and to improve their code by instructors. Develops an excellent program result by comparing it with other student code in the tournament environment by fighting codes.

Adapting gamified learning systems using educational data mining techniques

AbstractArtificial intelligence (AI) provides opportunities to improve the effectiveness of e‐learning by increasing students' engagement. Adaptive e‐learning uses AI to support individual learners by responding to their different learning needs which can be determined by analyzing their navigation history of e‐learning systems using data mining methods. Educational data mining (EDM) discovers new patterns of learning and teaching to facilitate the process of decision‐making to serve education improvement. Gamification is another way of increasing students' engagement by using game elements in a nongame context. In this paper, the gamification technique and EDM methods were used in combination with adaptive learning to increase the students' engagement and learning performance. An adaptive gamified learning system (AGLS) was developed which combines gamification, classification, and adaptation techniques to increase the effectiveness of e‐learning. This paper studies the impact of gamification and adaptive gamification on the effectiveness of e‐learning through increasing students' engagement and learning performance. AGLS was applied to the data structure course. Results showed that adaptive gamification has a positive effect on students' engagement and learning performance compared to just gamification.