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As global issues such as climate change, economic growth, social equality and the wealth gap are widely discussed, education for sustainable development (ESD) allows every human being to acquire the knowledge, skills, attitudes and values necessary to shape a sustainable future. It also requires participatory teaching and learning methods that motivate and empower learners to change their behavior and take action for sustainable development. Teachers have begun rating pupils based on peer assessment for open evaluation. Peer assessment enables students to transition from passive to active feedback recipients. The assessors improve critical thinking and encourage introspection, resulting in more significant recommendations. However, the quality of peer assessment is variable, resulting in reviewers not recognizing the remarks of other reviewers, therefore the benefits of peer assessment cannot be fulfilled. In the past, researchers frequently employed post-event questionnaires to examine the effects of peer assessment on learning effectiveness, which did not accurately reflect the quality of peer assessment in real time.

This study employs a multi-label model and develops a self-feedback system in order to use the AIOLPA system in the classroom to enhance students' learning efficacy and the validity of peer assessment.

The research findings indicate that the better peer assessment through the rapid feedback system, for the evaluator, encourages more self-reflection and attempts to provide more ideas, so bringing the peer rating closer to the instructor rating and assisting the evaluator. Improve self-evaluation and critical thinking for the evaluator, peers make suggestions and comments to help improve the work and support the growth of students' learning effectiveness, which can lead to more suggestions and an increase in the work’s quality.

ESD consequently promotes competencies like critical thinking, imagining future scenarios and making decisions in a collaborative way. This study builds an online peer assessment system with a self-feedback mechanism capable of classifying peer comments, comparing them with scores in a consistent manner and providing prompt feedback to critics.

This section describes sustainable development (SD) in relation to infrastructure projects and explains how to evaluate SD. SD is assessed as context-dependent, considering the project’s economic, social, and ecological context. Sustainable road infrastructure projects (SRIP) should encapsulate the complete life cycle from idea to development, functionality, and maintenance. SD should be considered as part of the evaluation process prior to project execution, but it can also serve other functions. Sustainability evaluation must start with project appraisal or evaluation and the earliest stages of project decision-making. Sustainable infrastructure projects (SIPs) are evaluated using a variety of techniques and models, such as cost-benefit analysis (CBA), multi-criteria techniques, ecological and societal impact assessments, ranking techniques, models, and evaluation guidelines. Established SD structures and modelling techniques for infrastructure projects are presented from an SD perspective, with the primary objective of investigating how they operate and determining whether existing models provide an effective method for applying the SD idea into infrastructure development. CBA is a widely used strategy for evaluating alternatives to maximize sociocultural well-being. It is based on the likelihood of costing customer advantages and negative impacts and has been discussed in scholarly articles. The multi-criteria decision analysis (MCDA) approach is an acceptable methodology for addressing complex matters involving high risk, conflicting objectives, different types of information and data, different concerns and points of view, and the representation of complex and evolving biological, ecological, and financial frameworks. It combines many methodologies and offers various advantages over more conventional ways of decision-making and plan development. It should be used to increase community participation and empower partner organizations and should apply several criteria at the same time, including those that are difficult to adjust and quantify. The key difficulty with this strategy is the usage of weightings, which has been sharply criticized by several researchers. Life-cycle assessment (LCA) is an adaptive tool used to assess the ecological effects of a particular action, task, or procedure. It is applied globally to decision-making in numerous fields, including transportation, energy, and water, and has become a typical tool for determining the ecological performance of infrastructure projects. However, it has a few flaws and could benefit from improvements to assess SD with greater precision. It is a fragmented mechanism for assessing the three components of SD, but its incorporation into other evaluation approaches is desirable. The evaluation of societal implications has been conducted using a variety of methods and techniques, but there is currently no standard method for assessing the communal and appropriation consequences of infrastructure initiatives. Social life-cycle assessments (SLCAs) are advancing, but consensus remains a challenge. The Evaluation Partnership and the Centre for European Policy Studies identified several obstacles and challenges to implementing an outstanding societal assessment, such as the term ‘societal impacts’ being potentially overbroad and not adequately defined, and the lack of a suitable method for quantitatively evaluating sociological effects. Additionally, a large section of societal assessments is biased and frequently inconsequential. The chapter discussed the theoretical and methodological stances on sustainable road infrastructure, using current SID concepts and evaluation techniques thoroughly.

The purpose of this paper is to describe this new evaluation experience with portfolio in economics, not only from the teacher’s point of view but from the student perspective, and all the learning from its implementation; to provide ideas of evaluation practices in virtual and face-to-face modality in international business education; to motivate the rethinking of assessment practices in higher education to combine the best of each modality in the future.

The present work is a case study based on a qualitative description of the implementation of a portfolio as an assessment practice, supported by a reflection questionnaire with students’ perceptions and some elements of metacognition. The first section summarizes the literature used as a theoretical framework of this work. The second section describes the portfolio implementation by analyzing teachers and students reflections with a qualitative approach. The third section presents the findings. The fourth section is a discussion of findings, practical implications, limitations and future research directions. Finally, the conclusions of the work are shared.

Because the portfolio has had overwhelming results to assess what students have learned during the pandemic, it has become the learning and assessment tool after the pandemic, as it transforms the classes experience by shifting the focus from traditional examinations to more comprehensive, personalized and reflective ones. It also empowers students to take ownership of their learning, develop essential skills and cultivate a deeper understanding. Among other benefits, the portfolio means the creation of a safe and supportive environment for honest reflection, the development and design of strategic directions to improve learning and lead students toward metacognitive autonomy. Reflection pieces, a critical component of the portfolio, are a vital tool in the proactive learning process, as through reflection students learn to examine their own performance and discuss strategies to enhance their success in future work.

This work began as an educational experience per se, not for research purposes, which caused it to be systematized and reconstructed in a descriptive way, not to measure quantitative results. In this way, the present work describes that the portfolio helps to achieve better results on students’ learning than traditional examinations but, as another limitation, it does not measure them nor the process. One more limitation of this work is that it was written in a postpandemic context but was implemented during the pandemic; therefore, the circumstances of writing are not the same as those of implementation, and this could also entail a certain margin of decontextualization. At the same time, this is an experience that is still in process and continually being adapted to this changed and changing educational postpandemic context.

One of the main implications of the portfolio experience, transferable to all educational contexts, is that it transforms the final exam into a metacognitive one, letting students be aware of their own process of learning and results – objectives and competences – acquired. In this way, it lets teachers witness a part of the learning process that is not so evident in the traditional assessment practices – focused on some aspect of the learning – as it makes visible the way in which students receive, process and apply content, that is to say, how they make it their own.

The portfolio promotes reflective learning and metacognition, vital skills that benefit students beyond the classroom. This can have a positive impact on societal attitudes toward education and the quality of learning. Of the students, 82% felt the portfolio creation was helpful in their personal and professional lives, suggesting a broader societal impact. The paper’s findings contribute to the body of knowledge about the effectiveness of portfolio-based assessment in higher education, especially in the worldwide transition from online education to postpandemic education. This could guide future studies in similar educational contexts or with different pedagogical innovative tools.

In light of the 2020 pandemic lockdown, this work delves into the pressing need for educators to adapt and modify their teaching approaches. The relevance of this study is accentuated by the worldwide transition from online education to postpandemic education. This paper bridges the gap between theory and practice because the research can be applied to the educational practice of any international business education context, as well as lay the foundations for future research in the field that contributes to increasing evidence of the effectiveness of the use of the portfolio to achieve significant and deep learning in higher education.

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

The study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.

Results suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.

Research on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.

Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation process to ensure remedial actions. This study aims to conduct an experimental research to detect anomalies in the evaluation methods.

Experimental research is conducted with scientific approach and design. The researchers categorized anomaly into three categories, namely, an anomaly in criteria assessment, subject anomaly and anomaly in subject marks allocation. The different anomaly detection algorithms are used to educate data through the software R, and the results are summarized in the tables.

The data points occurring in all algorithms are finally detected as an anomaly. The anomaly identifies the data points that deviate from the data set’s normal behavior. The subject which is consistently identified as anomalous by the different techniques is marked as an anomaly in evaluation. After identification, one can drill down to more details into the title of anomalies in the evaluation criteria.

This paper proposes an analytical model for the course evaluation process and demonstrates the use of actionable analytics to detect anomalies in the evaluation process.

This study aims to gain insights into how students perceive online examination practices and evaluation, as well as identify the key factors that impact their intentions toward online exams.

This empirical study conducted in India utilized an online survey method between May 24 and June 14, 2022. The data were collected from 712 undergraduate and postgraduate students through a Google Docs questionnaire. Descriptive analysis, exploratory factor analysis (EFA), regression and reliability analysis were conducted to analyze the data.

There are many studies related to the adoption of online learning. However, there are limited studies that explore the adoption and acceptance of online examinations and evaluations. This paper intends to fill this gap and tries to highlight important factors influencing online examination usage by the students. Five important factors emerged in the study include “pedagogy, validity, reliability,” “practicality,” “security,” “facilitating condition” and “intention,” The study concludes that taking tests online is preferred for several reasons, including the perceived increased dependability of grades and the decreased cost, time and effort associated with taking exams.

This research explores students' perceptions of learning management systems (LMS)-based online exams and provides insights for students to better prepare and adapt. It assists policymakers in developing fair assessment policies and improving LMS platforms. The study contributes to the theoretical understanding of e-learning and calls for further research to enhance the effectiveness of online exams. It identifies gaps in existing literature and recommends larger sample sizes, broader representation, longitudinal data collection and conversational methods for future research.

This study shows increasing research on e-learning and online examinations, mostly using quantitative methods. The important variables for understanding students' perceptions of online exams are “pedagogy, validity, reliability,” “practicality,” “security,” “facilitating condition” and “intention.” Concerns raised include validity, fairness and security. Electronic exams are more suitable for formative evaluation, and validity, reliability, security and adaptability are key pillars for successful online testing.

Increased emphasis on offering quality education underscores the need for developing a rigorous process for assessing academic programs in higher education. In this chapter, we develop a practical and rigorous framework for comprehensive assessment of academic programs. This framework generates in-depth communication between the academic departments and the university administration. It provides a useful tool for advancing the university mission, setting priorities, allocating resources, and identifying future areas of potential growth. This data-driven framework covers a wide range of qualitative and quantitative variables. To ensure a smooth and efficient implementation of the assessment process we present the critical stages in the development of a successful program assessment framework − from determining the assessment criteria, establishing the organizational climate, appointing the assessment committee, preparing program self-studies, to collecting and analyzing data. We present real examples from the author’s home institution to illustrate and support the reader’s understanding of the framework.

School teachers had difficulty in monitoring and assessing the students during the COVID-19 lockdown. This study attempts to understand the assessment and teaching challenges during COVID-19 and how this problematic situation was reshaped by new normal teaching.

The qualitative research included perspectives of 84 school teachers using an interview-based approach. Pedagogical assessment, societal assessment and personal assessment were considered in the study.

Content switching, student involvement and individual assessment are the challenges in terms of pedagogy. Buying power of electronic equipment, digital skills, study environment and distraction are the challenges faced in terms of personal challenges. Personal connect and interaction intensity is the challenges faced in terms of social challenges. Teachers adapted to synchronous, blended, flipped and asynchronous modes of teaching.

The authors’ study will support schools in developing their institutional plans to understand teachers' apprehensions better and to check the readiness of the schools towards teaching and learning approaches.

The purpose of this study is to generate knowledge about assessment methods in blended business education, which have become increasingly important to establish sustainable assessment practices that support knowledge acquisition for undergraduate students in business administration at a Colombian university.

For the analysis, a two groups comparison was performed using a nonequivalent control group design with a sample of 420 students. As this study wants to find insights to improve the knowledge on assessment topics in marketing research (MR) education, it was focused on the students from the business administration program. This study also uses individual scores from the state test as prior cognitive scores and the high school classification provided by the National Ministry of Education in Colombia (2012).

It was found that the variables that best predict performance on the MR course examinations were the mathematics skills and reading comprehension scores on the state test. The study also showed a better performance of female students on both assessment methods. There were no significant differences between the assessment methods or among the high school levels.

One of the limitations of this study is the limited number of items on the tests. Additionally, the authors recommend conducting an analysis of the differences between the testing items to provide a detailed explanation of students’ performance when comparing computer-based testing and paper-and-pencil testing.

Further design of teaching material and assessments online and offline, based on local and regional marketing problems, is suggested. As the current text and readings are more oriented to the English-speaking contexts, most of the problems presented are oriented to multinational companies and brands.

Insights into the skills required for future jobs provide valuable guidance (World Economic Forum, 2020). Essential skills for emerging roles, like data scientists, can find robust support within the MR course. To further enrich in-class and online exercises with Excel and SPSS, Colombian educators can leverage data sets obtained from sources like the national statistics office and international market intelligence databases available through the university’s library, including Passport and Statista. Engaging with authentic data sets provides students with a more profound understanding of practical applications in MR.

This approach facilitates the identification of key variables, such as assessment and cognitive abilities in math and reading, which predict students’ knowledge acquisition in MR. It not only offers insights into the relevant factors influencing learning in MR but also provides valuable feedback. Additionally, it suggests potential avenues for future research in this field.

Drawing on the elaboration likelihood model (ELM), the authors examine the influence of perceived professor teaching qualities, as central cues, on online professor ratings. Also, our study investigates how the volume and period of reviews, as peripheral cues, affect online professor ratings.

Leveraging stratified random sampling, the authors collect reviews of 892 Information Systems professors from 250 American universities. The authors employ regression models while conducting robustness tests through multi-level logistic regression and causal inference methods.

Our results suggest that the central route from perceived professor qualities to online professor ratings is significant, including most qualitative pedagogical factors except positive assessment. In addition to course difficulty, the effect of the peripheral route is limited due to deficient diagnosticity.

Our primary concern about the data validity is a lack of a competing and complementary dataset. However, an institutional evaluation survey or an experimental study can corroborate our findings in future research.

Online professor review sites can enhance their perceived diagnosticity and credibility by increasing review vividness and promoting site interactivity. In addition to traditional institutional evaluations, professors can obtain insightful feedback from review sites to improve their teaching effectiveness.

To our best knowledge, this study is the first attempt to employ the ELM and accessibility-diagnosticity theory in explicating the information processing of online professor reviews. It also sheds light on various determinants and routes to persuasion, thus providing a novel theoretical perspective on online professor reviews.