Improving Learning Outcomes in UML Sequence Diagrams Through Reduced Cognitive Load

This paper demonstrates how cognitive load theory can be used to improve learning outcomes by presenting a tool capable of assisting novices to learn to model sequence diagrams effectively. Sequence diagrams are known to lead to heavy cognitive load as they must be consistent with the class diagram, while discharging all the responsibilities specified in the underlying use case. Moreover, novices must also consider the various design options and their impact on the qualitative aspects of the model. Our tool allows cognitive load to be better managed by using a ‘divide and conquer’ approach. In the initial stage students need to focus only on consistency aspects, and they will not be allowed violate the constraints stated in the class diagram. In the second stage, students will not be allowed to submit a diagram until the stated use case goals are met. In the final stage qualitative feedback and marks are awarded based on established metrics and students are allowed to improve their scores by resubmitting the model. Qualitative and quantitative results show that our novel tool using a form of gamification has helped to improve the learning outcomes in modelling substantially, especially for the stragglers. One benefit of our approach is that it can be adapted to other areas where students maybe cognitively challenged

Meta-Information as a Service: A Big Social Data Analysis Framework

Social information services generate a large amount of data. Traditional social information service analysis techniques first require the large data to be stored, and afterwards processed and analyzed. However, as the size of the data grows the storing and processing cost increases. In this paper, we propose a ‘Meta-Information as a Service’ (MIaaS) framework that extracts the data from various social information services and transforms into useful information. The framework provides a new formal model to present the services required for social information service data analysis. An efficient data model to store and access the information. We also propose a new Quality of Service (QoS) model to capture the dynamic features of social information services. We use social information service based sentiment analysis as a motivating scenario. Experiments are conducted on real dataset. The preliminary results prove the feasibility of the proposed approach

A Protocol for Secrecy and Authentication within Proxy-Based SPKI/SDSI Mobile Networks

Resource-constrained mobile devices are becoming increasingly popular within distributed networks, but introduce a weak point of security. Existing protocols for distributed mobile device networks, such as SPKI/SDSI, are emerging standards and lack built-in confidentiality, mutual authorisation and mutual authentication. Our research addresses the above-mentioned security limitations of an existing network security protocol for distributed mobile device networks. By securing the protocol and minimising exchanged messages, our work gives a result which is both faster than the current protocol and more secure. This will open up new application areas for SPKI/SDSI.

Scaffolded Approach to Reduce Cognitive Load for Modelling Sequence Diagrams

Providing formative feedback on heavy cognitive tasks, such as modelling sequence diagrams, is important for novices to improve learning outcomes. However, reduced face-to-face contact since the advent of COVID-19, has made it difficult to give the feedback and instead made it necessary to devise pedagogical tools able to give regular formative feedback. This paper summarizes and reports the longitudinal studies undertaken to develop such a tool capable of verifying consistency, completeness and quality. This paper also reports views of experienced software engineering instructors about the effectiveness of the current pedagogical tool on improving overall learning outcomes. Based on these findings, we have proposed a number of metrics and incorporated them into our pedagogical tool. The novelty of this tool is its inherent scaffolded approach giving feedback on consistency with class diagrams, verifying responsibilities specified in use-case postconditions are discharged and generating qualitative feedback based on established metrics

Improving Industry Relevance and Reducing Plagiarism using Milestones and Video Reflections

In this paper we report our experience of substantially reducing the extent of assignment plagiarism during the COVID-19 affected period. We did this by decomposing the authentic assignment into five formative milestones emphasizing higher order thinking. Students had to submit five three-minute videos demonstrating how each milestone was reached, why the particular strategy was adapted and what could have been done better. We evaluated the effectiveness of our approach by comparing the assignment plagiarism pattern with the previous COVID-19 affected semester and by correlating the performance in formative tasks with performance in the final randomized test. Our results suggest formative video submissions can substantially reduce plagiarism in project-based courses and can reduce the reliance on the final test for measuring the learning outcomes. The survey on student perceptions reveals use of video milestone submissions helped to improve motivation, self-efficacy and learning outcomes

ART- An Instrument for Developing Algorithmic Reasoning in Programmers

Despite the significant advances in Information systems adopted for several different courses, the failure rate for Introductory Programming Courses (IPCs) still remains high. At present, the formative activities used in IPCs focus on tracing tasks. However, there is no clear evidence that such tasks foster higher-level abstraction and cognitive reasoning skills needed for code writing. We propose an Algorithmic Reasoning Task system (ARTs) as an instrument, that can be adapted by existing information systems to develop reasoning skills for students learning programming. Our analysis of novice programmer performance reveals that code-writing tasks correlate higher with Algorithmic Reasoning Tasks (ARTs) than with traditional tracing tasks