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L. Dee Fink proposes that different and more significant kinds of learning should be created in higher education to transition student outcomes from simply “learning” to “significant learning,” and these new types of learning should be situated within significant learning experiences (Fink, 2003). Fink also identified a taxonomy of significant learning that included six components: integration, foundational knowledge, application, human dimension, caring, and learning how to learn. Using Fink’s Taxonomy of Significant Learning as a framework, the authors will share the development of a course on navigating the US Healthcare System that resulted in significant learning outcomes for students completing the first semester of a four-year Doctor of Pharmacy curriculum. Each learning experience will link to a component of the taxonomy and will serve as the mechanism for the authors to share the development and implementation associated with each aspect of the semester-long course. The assessment structure of the course is described in detail. The authors present one or more learning experiences to illustrate each component of Fink’s Taxonomy. Finally, lessons learned from the development and implementation of the course are presented to guide programs considering implementation of a similar significant learning experience.

With the outbreak of the COVID-19 pandemic, online assessment has become the dominant mode of examination in higher education institutions. However, there are contradictory findings on how students perceive online assessment and its impact on their academic performance. Thus, the purpose of this study is to evaluate the potential impact of online assessment on students' academic performance.

This study proposes a research model based on the task–technology fit theory and empirically validates the model using a survey from students in the UK. In addition, the study conducted four experiments based on paper-based and online assessments and analysed the data using paired sample t test and structural equation modelling.

The findings show that the use of online assessment has a positive impact on students' academic performance. Similarly, the results from the experiment also indicate that students perform better using online assessments than paper-based assessments.

The findings provide crucial evidence needed to shape policy towards institutionalising online assessment. In addition, the findings provide assurance to students, academics, administrators and policymakers that carefully designed online assessments can improve students' academic performance. Moreover, the study also provides important insights for curriculum redesign towards transitioning to online assessment in higher education institutions.

This study advances research by offering a more nuanced understanding of online assessment on students' academic performance since the majority of previous studies have offered contradictory findings. In addition, the study moves beyond existing research by complementing assessment results with the views of students in evaluating the impact of online assessment on their academic performance. Second, the study develops and validates a research model that explains how the fits between technology and assessment tasks influence students' academic performance. Lastly, the study provides evidence to support the wide use of online assessment in higher education.

This article presents our experience in implementing the assessment for learning process (AfL) to enhance the teaching–learning quality, which has faced numerous challenges impacting educational quality. The effectiveness of this technique is demonstrated through a case study conducted in a core course of chemical engineering.

The article shares insights into the systematic course design and planning processes that were discussed and developed through AfL practices. Significant emphasis is placed on implementing formative and summative student self-assessment surveys as simple yet effective methods to meet this purpose. Quantitative data were collected and analyzed over three consecutive academic years (2020–2022) using various statistical parameters such as percentage, interquartile range and the program’s numerical goal (%G).

The AfL process via formative and summative surveys could significantly and effectively improve teaching–learning quality. These findings assist educators in identifying appropriate teaching methods and recognizing areas of weakness and strength, thereby facilitating continuous improvement in the teaching–learning quality. Validation methods, including quizzes and numerical grades, were employed to practically verify the outcome obtained from the questionnaires.

The AfL techniques demonstrated in this study can be directly implemented or adapted for various educational fields to enhance the teaching–learning quality.

The practical implementation of AfL in an engineering context has hardly been reported, particularly in chemical engineering. This work represents the practical implementation of AfL to enhance engineering field education.

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.

This paper aims to propose an innovative method for deploying a personalized instructor-created software-aided assessment system, that will disrupt traditional learning environments by allowing students to confidentially and with indirect supervision from the instructor, assess their knowledge and ability to achieve the course outcomes.

Through empirical evaluation in real-world educational settings, the authors examine the impact of augmenting human activity in the classroom with an innovative software platform to transform the learning process.

Findings indicate that this software-aided assessment system effectively augments human interactivity by providing timely instructor-designed feedback to increase knowledge retention and skillsets.

This study has shown that incorporating disruptive innovation through the use of software-aided assessment systems increases the effectiveness of the faculty in the classroom and enhances student learning and retention. Thus, a transformative software-aided assessment system design that incorporates artificial intelligence into the learning pathway should be pursued. These software-aided assessments are disruptive innovation as they are formative, frequent and require little direct involvement from the instructor.

To the best of the authors’ knowledge, this study is the first of its kind to incorporate artificial intelligence into the assessment process by analyzing results of pilot programs at several universities. The results demonstrate how using software-aided transformative assessments in various courses have helped instructors assess students’ preparedness and track their learning progress. These software-aided systems are the first step in bringing disruptive innovation to the classroom as these software-aided assessment instruments rapidly assess learners’ knowledge and skills based on short, easily created, multiple-choice tests, with little direct engagement from the faculty.

The availability of daily assessment data in a centralized monitoring system at school provides the opportunity to detect unusual scores soon after the assessment is carried out. This paper introduces a model for the detection of unusual scores of individual students to immediately improve performances that deviate from a normal state.

A student's ability, a subject's difficulty level, a student's specific ability in a subject, and the difficulty level of an assessment in a subject are selected as factor effects of a linear ANOVA model. Through analysis of variance, a case study is conducted based on 330 data points of assessment scores of primary grade students retrieved from an international school in Japan.

The actual score is below the lower control limit, which is recognized as an unusual score, and the score can be detected immediately after sitting for an assessment and is beneficial for students to take immediate remedies based on daily assessment. This is demonstrated through a case study.

The detection of unusual scores based on a linear model of individual students soon after each assessment benefits from immediate remedy aligns with a daily management concept. The daily assessment data in a school system enable detection based on individual students, subject-wise and assessment-wise to improve student performances in the same academic year.

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.

Lack of appropriate student support and drawbacks in academic progression signify the importance of enhancing assessment and feedback in higher education (HE). Although assessment and feedback are significant in HE, minimal empirical research holistically explores the best practices. This study aims to address the niche and develop a decisive guideline for enhancing assessment setting and feedback provision within HE curricula.

A systematic approach was taken to obtain data for the study: a literature review underpinning the thematic content analysis of study documents, followed by semi-structured interviews. Document analysis contained mid-module reviews/student feedback; rubrics used in assessment; and formative/summative feedback provided for the graded work. Documental analysis informed the key attributes of the semi-structured interview. Interpretive structural modelling (ISM) analysis identified the influence and reliance of each driver.

This study revealed 15 drivers – 4 fundamental, 6 significant and 5 important – for enhancing assessment and feedback. The level partitioning from the ISM analysis established that all assessment and feedback needs to be underpinned by the university policy and fed into the assessment regime and marking scheme. This study identified that National Student Survey results were significantly improved due to implementing said drivers compared with the national and sector benchmarks.

The developed drivers enable the best practices in assessment setting and feedback provision. The level partition diagram can be used as a decisive guideline or a provisional framework in assessment and feedback provision for quality assurance in HE.

This study is one of, if not the only, to develop a guideline for signposting drivers and their influence and reliance to enhance assessment and feedback in a holistic HE setting. The developed drivers and the level partition diagram bring novelty and add to the current body of knowledge.

Behavioral threat assessment and intervention is a violence prevention strategy that has become widely used in US schools. Instead of relying on a zero tolerance disciplinary approach that often leads to school exclusion of students with disabilities, schools can use a multidisciplinary threat assessment team to assess the seriousness and context of a student's behavior and identify appropriate interventions and supports. This process is especially valuable for students with disabilities who are identified as threatening violence. This chapter describes an evidence-based model of threat assessment, the Comprehensive School Threat Assessment Guidelines (CSTAG), ¹ reviews some common misunderstandings of threat assessment, and presents a case example illustrating the value of threat assessment in preventing school exclusion, and provides services for a student with a disability.

In higher education, the usual assessment methods are oral or written exams, multiple-choice questions, and individual or group written essays. However, in a distance learning context, it is often necessary to offer students more support, including formative assessment and self-assessment strategies. International reports have shown that teachers have adapted their learning assessment strategies during the Covid-19 pandemic. How did educators adapt their assessment strategies recently during the pandemic? What are the intentions underlying their decision-making? Our objective is to understand the decision-making process of teacher educators and university teachers in adapting their methodology for assessing student learning during the pandemic. To answer these questions, we adopted a qualitative research approach. We collected data from 29 different countries via: (1) open-ended questionnaires, (2) personal accounts, (3) unstructured interviews, and (4) a specific questionnaire about assessment. Four main categories emerged from our data: (1) challenges, (2) assessment practices, (3) changes in teachers' perceptions and practices, and (4) reflection on assessment. The findings suggest that (1) uses of technology for assessment have developed strongly; (2) careful coordination among colleagues is very important; (3) educators developed formative assessment strategies; and (4) educators' reflections have focused on many challenges: ethical, technical, and pedagogical.