Proceedings of world academy of science, engineering and technology, vol 8

This article is a survey of program visualization systems intended for teaching beginners about the runtime behavior of computer programs. Our focus is on generic systems that are capable of illustrating many kinds of programs and behaviors. We inclusively describe such systems from the last three decades and review findings from their empirical evaluations. A comparable review on the topic does not previously exist; ours is intended to serve as a reference for the creators, evaluators, and users of educational program visualization systems. Moreover, we revisit the issue of learner engagement which has been identified as a potentially key factor in the success of educational software visualization and summarize what little is known about engagement in the context of the generic program visualization systems for beginners that we have reviewed; a proposed refinement of the frameworks previously used by computing education researchers to rank types of learner engagement is a side product of this effort. Overall, our review illustrates that program visualization systems for beginners are often short-lived research prototypes that support the user-controlled viewing of program animations; a recent trend is to support more engaging modes of user interaction. The results of evaluations largely support the use of program visualization in introductory programming education, but research to date is insufficient for drawing more nuanced conclusions with respect to learner engagement. On the basis of our review, we identify interesting questions to answer for future research in relation to themes such as engagement, the authenticity of learning tasks, cognitive load, and the integration of program visualization into introductory programming pedagogy.

A Review of Generic Program Visualization Systems for Introductory Programming Education

A wide range of quantitative and qualitative modelling research on ecosystem services (ESS) has recently been conducted. The available models range between elementary, indicator-based models and complex process-based systems. A semi-quantitative modelling approach that has recently gained importance in ecological modelling is Bayesian belief networks (BBNs). Due to their high transparency, the possibility to combine empirical data with expert knowledge and their explicit treatment of uncertainties, BBNs can make a considerable contribution to the ESS modelling research. However, the number of applications of BBNs in ESS modelling is still limited. This review discusses a number of BBN-based ESS models developed in the last decade. A SWOT analysis highlights the advantages and disadvantages of BBNs in ESS modelling and pinpoints remaining challenges for future research. The existing BBN models are suited to describe, analyse, predict and value ESS. Nevertheless, some weaknesses have to be considered, including poor flexibility of frequently applied software packages, difficulties in eliciting expert knowledge and the inability to model feedback loops. BBNs are increasingly used to analyse, predict and value ecosystem services (ESS).Most BBN applications in ESS modelling target only a single service.Numerous advantages of BBNs in ESS modelling are demonstrated in current applications.Model drawbacks are absence of feedback loops and obligatory variable discretization.Spatially explicit modelling and modelling of ESS bundles are future opportunities.

A review of Bayesian belief networks in ecosystem service modelling

This article brings together, summarizes, and comments on several threads of research that have contributed to our understanding of the challenges that novice programmers face when learning about the runtime dynamics of programs and the role of the computer in program execution. More specifically, the review covers the literature on programming misconceptions, the cognitive theory of mental models, constructivist theory of knowledge and learning, phenomenographic research on experiencing programming, and the theory of threshold concepts. These bodies of work are examined in relation to the concept of a “notional machine”—an abstract computer for executing programs of a particular kind. As a whole, the literature points to notional machines as a major challenge in introductory programming education. It is argued that instructors should acknowledge the notional machine as an explicit learning objective and address it in teaching. Teaching within some programming paradigms, such as object-oriented programming, may benefit from using multiple notional machines at different levels of abstraction. Pointers to some promising pedagogical techniques are provided.

Notional machines and introductory programming education

Formative feedback, aimed at helping students to improve their work, is an important factor in learning. Many tools that offer programming exercises provide automated feedback on student solutions. We have performed a systematic literature review to find out what kind of feedback is provided, which techniques are used to generate the feedback, how adaptable the feedback is, and how these tools are evaluated. We have designed a labelling to classify the tools, and use Narciss’ feedback content categories to classify feedback messages. We report on the results of coding a total of 101 tools. We have found that feedback mostly focuses on identifying mistakes and less on fixing problems and taking a next step. Furthermore, teachers cannot easily adapt tools to their own needs. However, the diversity of feedback types has increased over the past decades and new techniques are being applied to generate feedback that is increasingly helpful for students.

https://dl.acm.org/doi/pdf/10.1145/3231711

A Systematic Literature Review of Automated Feedback Generation for Programming Exercises

In this paper, an efficient and scalable technique for computer network security is presented. On one hand, the decryption scheme and the public key creation used in this work are based on a multi-layer neural network that is trained by backpropagation learning algorithm. On the other hand, the encryption scheme and the private key creation process are based on Boolean algebra. This is a new potential source for public key cryptographic schemes which are not based on number theoretic functions and have small time and memory complexities. This paper along with test results show that the possibility of guessing keys is extremely weaker than using the Data Encryption Standard method (DES), which is a widely-used method of data encryption. The presented results are obtained through the use of MATLAB 6.5.1 software.

A Backpropagation Neural Network for Computer Network Security

Program diagnosis systems were developed to help users solve programming problems. By providing guidence on errors and misconceptions, these systems can help the users in writing programs and understanding their dynamic behavior. Features of software visualization which aim at providing visual and concrete depictions to the abstractions and operations of programs have also shown to be making programs more understandable. The main theme of this paper is to asses the usefulness of incorporating features of software visualization into the design of program diagnosis systems intended for novices. We report an empirical evaluation to assess the effectiveness of supporting visualization features during problem solving. The system used in the evaluation integrates visualzation and immediacy features and supports a model-tracing based approach to program diagnosis. Unlike other similar systems, our prototype system supports a more flexible style of interaction by increasing the grain size of diagnosis to a complete programming statement. The evaluation reported here seems to suggest that when supported with visualization features, systems for program diagnosis tend to be more effective in helping the users during problem solving.

Incorporating Software Visualization in the Design of Intelligent Diagnosis Systems for User Programming

We describe the development and application of a prototype Dynamic Bayesian Network (DBN) that models groundwater quality efficiently. First, we present a new technique for data preprocessing that is needed when groundwater databases are used as the data source to learn the probabilities for dynamic decision models. Then we describe the network models we developed, as well as the methods we are used. Various challenges, such as acquiring groundwater datasets and identifying pollutants and anticipating potential problem contaminants, are addressed. Finally, we present the results of applications of these models.

Dynamic modeling of groundwater pollutants with bayesian networks

Improving Clustering Performance by Using Feature Selection and Extraction Techniques

A new methodology is developed to analyse existingwater quality monitoring networks. This methodology incorporates different aspects ofmonitoring, including vulnerability/probability assessment, environmental health risk, the value of information, and redundancy redu ction. The work starts with a formulation of a conceptual framework for groundwater quality monitoringto represent the methodology’s context. This work presents the development of Bayesian techniques for the assessment of groundwater quality. The primary aim is to develop a predictive model and a computer system to assess and predict the impact of pollutants on the water column. The process of the analysis begins by postulating a model in light of al available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior distribution of the model parameters is then combined with the new data through Bayes’ theorem to yield the current knowledge represented by the posterior distribution of model parameters. This process of updating information about the unknown model parameters is then repeated in a sequential manner as more and more new information becomes available.

http://www.airccse.org/journal/ijsc/papers/5214ijsc03.pdf

Khalil Shihab

Papers

A Backpropagation Neural Network for Computer Network Security

Incorporating Software Visualization in the Design of Intelligent Diagnosis Systems for User Programming

Dynamic modeling of groundwater pollutants with bayesian networks

Improving Clustering Performance by Using Feature Selection and Extraction Techniques

An efficient method f or assessing water quality based on bayesian belief n etworks