Academic literature on the topic 'Automated Proctoring'

AbstractWith the upcoming enforcement of the EU AI Act, documentation of high-risk AI systems and their risk management information will become a legal requirement playing a pivotal role in demonstration of compliance. Despite its importance, there is a lack of standards and guidelines to assist with drawing up AI and risk documentation aligned with the AI Act. This paper aims to address this gap by providing an in-depth analysis of the AI Act’s provisions regarding technical documentation, wherein we particularly focus on AI risk management. On the basis of this analysis, we propose AI Cards as a novel holistic framework for representing a given intended use of an AI system by encompassing information regarding technical specifications, context of use, and risk management, both in human- and machine-readable formats. While the human-readable representation of AI Cards provides AI stakeholders with a transparent and comprehensible overview of the AI use case, its machine-readable specification leverages on state of the art Semantic Web technologies to embody the interoperability needed for exchanging documentation within the AI value chain. This brings the flexibility required for reflecting changes applied to the AI system and its context, provides the scalability needed to accommodate potential amendments to legal requirements, and enables development of automated tools to assist with legal compliance and conformity assessment tasks. To solidify the benefits, we provide an exemplar AI Card for an AI-based student proctoring system and further discuss its potential applications within and beyond the context of the AI Act.

Online proctoring tools and techniques have gained significant attention in recent years due to the increasing popularity of online learning platforms and online assessments. This chapter provides an in-depth analysis of different online proctoring techniques and tools that are commonly used to maintain the integrity of online exams. The chapter begins by highlighting the importance of online proctoring in ensuring fair evaluation and preventing cheating. It then explores various types of online proctoring techniques such as live proctoring, automated proctoring, and recorded proctoring. The advantages and limitations of each technique are discussed, providing a comprehensive understanding of when and how to use them effectively. Overall, this chapter aims to empower educators and administrators with the knowledge and information needed to implement effective online proctoring methods. By understanding the various techniques and tools available, they can enhance the credibility and security of their online assessments, ensuring a fair and reliable evaluation process.

The use of online assessment methods in K12 computer science education, which allow students to be assessed via networked computers, increases efficiency and accuracy by facilitating data collection, analysis and reporting, and enables large-scale tests to be implemented easily and at low costs. Online assessment, which can be applied in different types such as diagnostic, formative and summative performance-based, criterion-referenced, self-assessment and peer assessment, can also be applied for different purposes such as online skills assessment, online aptitude assessment, online coding assessment, psychometric assessment. Traditional assessment tools such as online exams and standardized tests can be used for online assessment of K12 computer science education, while alternative assessment methods such as e-portfolios, digital rubrics, self-assessment and peer assessment can be preferred. Online exams, which are considered traditional assessment tools, stand out with different advantages such as the use of many question types in their structure, so that CS subjects with different content can be measured with question types suitable for their own dynamics, and the automatic evaluation and reporting of measurement results. In addition, online exams can be assigned as tasks to classes created in a virtual environment, their completion can be tracked, collective feedback can be provided and students' progress can be monitored. Standardized tests, which are generally used to assess cognitive skills, stand out with their features such as taking into account basic competence areas and being valid and reliable tools. Automated Assessment Tools, one of the alternative online assessment methods, are widely used for purposes such as the assessment of programming skills and code analysis, and provide many advantages such as making the quality of the assessment independent from the teacher and providing more qualified feedback to students, as well as saving educators from a significant workload. E-portfolios are more than just a space for students to accumulate their individual work, they can also be used for accreditation, application, graduate employability, evidence of professional competence, material repository, and many other purposes. Peer assessment makes important contributions to process assessment in CS education by using different types of assessment such as written assessment, evaluation of coding projects, debugging and algorithm analysis. Digital rubrics, on the other hand, are one of the alternative assessment tools frequently used in computer science education in order to understand at what level of expertise a learning outcome is acquired by students for which subheadings. Many different online assessment applications such as Rubric-Maker, Project Quantum, Computational Thinking Test, Bebras, LeetCode, Codetester, Mahara, etc. can be used in the assessment of K12 Computer Science education, which includes basic topics such as information technologies and information processing systems, computer networks, media creation and editing, data and analysis, algorithms and programming, ethics and cyber security. In this way, assessment designs can be created effectively and easily, ease of access can be provided, time and cost savings can be made, participation can be increased, and fast and advanced feedback can be provided. However, extra security measures such as authentication, plagiarism control, and proctored exams are needed to ensure the reliability of online assessment practices. Different types of proctored online exams such as live proctoring, recorded proctoring, and automated proctoring can be implemented; in addition, measures such as limiting the exam time, randomizing questions, locking internet browsers, voice recognition, face recognition, monitoring keyboard dynamics, and tracking typing style also positively affect the reliability of online exams. Online assessment methods have the potential to provide significant gains in terms of making a comprehensive assessment of students' knowledge, skills and competencies when applied in a contextually appropriate manner with strategies and tools appropriate to the outcomes in the K12 CS education content