Multidisciplinary University Research Articles

Exercise Habits and Mental Health Outcomes among Students in a Technology University in China

This study employed a descriptive-comparative-correlational research approach to examine the relationship between students' exercise habits and their mental health outcomes at a multi-disciplinary university in Zhengzhou, Henan Province. The college of physical education, which provides training across 22 colleges and 73 undergraduate majors, served as the study’s setting, involving a large sample of 32,760 undergraduate students. Using stratified random sampling, the researchers selected a representative sample of 380 students, with a 5% margin of error, to identify patterns and explore relationships between exercise habits and mental health. A structured questionnaire, developed to assess exercise habits and mental health, underwent content and face validation, with reliability measured using Cronbach’s Alpha (0.980 and 0.974). Data collection took place in person, with the results processed using SPSS and analyzed through means, standard deviation, and Pearson’s r correlation. Findings indicated significant positive correlations between various dimensions of exercise habits—including routine stability, goal persistence, adaptability to change, and social influences—and mental health outcomes such as stress levels, coping mechanisms, social support, sleep quality, and access to mental health resources. Notably, adaptability to change exhibited the highest correlation with coping mechanisms and overall mental health (r = 0.804), underscoring the importance of flexible exercise routines in managing mental health. The study’s insights emphasize that consistent, varied, and socially supported exercise habits are associated with reduced stress, enhanced coping strategies, and improved overall mental health. Promoting regular physical activity and addressing barriers to exercise can foster better mental health among students, suggesting the need for integrated physical and mental health programs within academic institutions.

Navigating Academic Horizons: The Role of Location-Based Games in Multidisciplinary University Transition

The study focuses on the development and evaluation of a location-based game called CMU Journey, which is aimed at helping first-year students become acquainted with their new university setting during the phase of transition. The study’s three primary goals are to: (1) create a location-based game to improve knowledge acquisition related to university transition for freshmen; (2) evaluate the performance of the developed location-based game in supporting knowledge acquisition relevant to university transition; and (3) explore students’ experiences and perspectives with the location-based game designed for university transition. Using a mixed-methods approach, the study collects quantitative data through pre-post measures as well as qualitative data synthesis using an online open-ended questionnaire. Participants in the study are from Chiang Mai University’s Humanities and Social Sciences (N = 434), Technology and Science (N = 341), and Medical and Health (N = 256) programs. The findings show that students who finished the program learned considerable information about starting university, had a favorable attitude regarding studying due to the location-based game, and experienced motivation. An examination of both quantitative data from surveys and qualitative feedback from utilized data found a mix of both positive and negative comments from users. The results presented indicate the potential of location-based games as a beneficial tool for assisting with university transition, along with opportunities for future game design and implementation improvements.

Entrepreneurial ecosystem and intentions of students in multidisciplinary universities: A study in Can Tho, Mekong Delta, Vietnam

Entrepreneurial ecosystem and intentions of students in multidisciplinary universities: A study in Can Tho, Mekong Delta, Vietnam

Paradigm Shift in Higher Education: National Education Policy (NEP 2020)

Education plays a crucial role in human development, serving the acquisition of knowledge and skills. The landscape of education in India has transformed, since independence. This transformation has touched upon various facets of education, reshaping its structure and priorities. The National Education Policy of 1986, along with a revised version in 1992, has been an integral part of educational discourse. The unveiling of the National Education Policy (NEP) in 2020 heralds a fresh vision for education in India. NEP 2020 envisions an education system starting from pre-primary to various aspects of higher education. The perspective of NEP 2020 is to cater to the unique capabilities of every student and their holistic development. NEP 2020 has opened doors to many initiatives for higher education that will impart quality education. NEP 2020 has proposed advancement in higher education: moving towards interdisciplinary and multidisciplinary education, faculty and institutional autonomy, various measures to enhance student experiences, formation of a single regulatory body for governing higher education, measures to include a large number of student enrolment by the means of online education and distance education. The researcher will highlight various recommendations of NEP 2020 concerning various suggestions enlisted in the policy for Higher education. The researcher will shed light on initiatives for multidisciplinary education, optimal learning environment, internationalization of higher education, equity and inclusion in higher education, and vocational education with effect from NEP 2020. The paper emphasizes the significant changes in higher education institutions, including the creation of multidisciplinary universities, colleges, and programs.

Navigating the Evolving Landscape of Teaching and Learning: University Faculty and Staff Perceptions of the Artificial Intelligence-Altered Terrain

This study examines the perspectives of university faculty and staff regarding the influence of artificial intelligence on the higher education teaching and learning landscape following the global launch of free-to-use OpenAI ChatGPT in the autumn of 2022. The participants were 79 university faculty and staff from diverse academic fields across all campuses of a multidisciplinary university in Finland. The data were collected in two phases in May–June 2023 and in March 2024, with focus group interviews and Learning Café discussions. The results showed that AI has a broad impact on teaching and studying in higher education. Six main categories were identified: (1) the impact of AI on students’ learning processes, (2) the impact of AI on teaching, (3) the knowledge required of future employees and the impact of AI on them, (4) ethical and economic issues, (5) the development of AI or its use in the future, and (6) the nature of the change brought about by artificial intelligence. AI is already making inroads into higher education, and participants underscored its dual impact on teaching and learning, highlighting both opportunities and challenges. While teachers recognized AI’s potential to enhance teaching and assessment methods, they also acknowledged the need to adapt their courses accordingly. They expressed concerns about understanding AI’s impact on students’ learning processes and their own contributions to learning assignments. The participants emphasized the necessity of providing support and training for teachers to ensure AI is meaningfully and effectively integrated into teaching and learning practices and landscapes.

Examining the Reliability and Validity of Measuring Scales related to Informatization Instructional Leadership Using PLS-SEM Approach

During the COVID-19, university teachers’ informatization instructional leadership (TIIL) was adopted in many countries. This attracted widespread attention. This research derived six factors from the unified theory of acceptance and use of technology (UTAUT) including performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC) and behavioral intention (BI), added two new internal elements related to the individual teacher which are computer self-efficacy (CSE) and blended teaching competence (BTC). Before using the Partial Least Squares Structural Equation Modeling (PLS-SEM) to explore the contributing factors to TIIL by assessing interrelation between constructs within extended UTAUT model in this study. This pilot study aimed to examine the reliability and validity of modified scales incorporating Use Expectancy (UE) Scale including (PE Scale and EE Scale) used to measure use expectancy, SI Scale to measure social influence, FC Scale to measure facilitating conditions, CSE Scale to measure computer self-efficacy, BTC Scale to measure blended teaching competence, BI Scale to measure behavioral intention to adopt TIIL, and the TIIL Scale to measure teachers’ informatization instructional leadership. A total of 60 teachers from the large multi-disciplinary private undergraduate universities in Xi’an city of Shaanxi province in China participated in this research. The data was collected in November-December 2022 during the middle stages of COVID-19 pandemic. The PLS-SEM approach was used to evaluate the reliability and validity of the adapted scales. The internal consistency reliability was determined by composite reliability (CR) and Cronbach’s alpha. Convergent validity was assessed by outer loading and average variance extracted (AVE). Assessment of discriminant validity was measured by Fornell-Larcker criterion, Cross-loadings and Heterotrait-Monotrait Ratio (HTMT). Results showed after deleting nine items with lower than .40, Cronbach’s alpha values were all higher than .70. CR values were at a satisfactory level. All item values fulfilled the criteria of AVE, Fornell-Larcker criterion, cross-loadings, and HTMT. Research results revealed all adapted scales were valid and reliable to be used in future research. This study explored the influencing factors of TIIL in Chinese context, enriched the theory of TIIL, and provided practical support for the future development of TIIL.

Vestibular Function: A Hidden Harbinger of Human Hypoxia

The aim of this study was to evaluate the impact of hypoxia on the human vestibular system — the organs in our inner ear that sense head motion and gravity. The vestibular system contributes to self-motion perception, balance control, and several reflexive responses. Maintaining ideal vestibular sensation imposes a high metabolic load; consider that the human vestibular periphery has over 100,000 hairs cells requiring energy to maintain depolarization and roughly 40,000 peripheral neurons requiring energy to support an average firing rate of nearly 100 action potentials per second for each neuron. Consistent with this, a recent study concluded that the vestibular organs of old animals appear to be bordering on an energy crisis; such an energy crisis could result from inadequate O2 or inadequate fuel. To quantify vestibular impacts of reduced O2, vestibular function was quantified using self-motion perceptual thresholds. Specifically, we used standard forced-choice psychophysical methods to determine the smallest earth-vertical (i.e., upward/downward) translation that a seated human could reliably sense. To quantify blood oxygenation, we used a standard fingertip pulse oximeter. To manipulate blood oxygenation, we controlled the oxygen (O2) air content using a Reduced Oxygen Breathing Device (Environics). Our hypotheses were that: (1) human vestibular thresholds will increase with reduced blood oxygenation, and (2) vestibular degradation will monotonically increase as blood oxygenation decreases. All data were collected at Wright Patterson Air Force Base (elevation 823’). On different days, participants breathed air having O2 content of 20.9%, 15.4%, 14.3%, 12.9%, 11.8%, and 10.7% — chosen to simulate O2 content found at altitudes of 0’ (“baseline”), 8,000’, 10,000’, 12,500’, 15,000’, and 17,500’. Fifteen participants completed test sessions at 20.9% and 15.4% O2. Earth-vertical translation thresholds were quantified to be 23.5% greater when the oxygen content was 15.4% than at baseline (p=0.005, Wilcoxson Sign Rank Test, Z = -2.84). While all subjects were unable to complete all conditions, a second set of participants was invited to complete test sessions at 20.9%, 14.3%, 12.9%, 11.8%, and 10.7% O2. Thresholds increased as O2 content decreased. Subjects reported no spatial disorientation. We did not vary barometric pressure, but these results show that vestibular thresholds are both significantly and substantively impacted at simulated altitudes of 8,000’. This is noteworthy because it matches the cabin pressurization required in the US by the FAA for commercial flights. These results suggest that many of us who have flown in a commercial jet may have experienced degraded vestibular function. Behavioral impacts remain to be understood, but vestibular function does appear to be a harbinger of human hypoxia. Support provided by Offce of Navy Research via a Multidisciplinary University Research Initiative (MURI N00014-20-1-2163). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Juxtaposing individual and group mobility from sparse Wi-Fi signatures with cloud-assisted computing: a case study for a multidisciplinary university campus

ABSTRACT Understanding human mobility undoubtedly enriches common goods. Among available location-aware technologies, Wi-Fi provides a more sustainable and high-resolution means to study human mobility patterns as it has become conspicuous and affordable recently. Whilst existing studies have shed light on facets of personal mobility, the intricate dynamics of group mobility have garnered comparatively scant empirical scrutiny in real-world settings, especially in the juxtaposition with individual mobility. Moreover, they have often overlooked the multifaceted nature of personal attributes influencing daily routines. This study introduces a comprehensive framework that takes advantage of the readily available Wi-Fi connection data and cloud-assisted computing for juxtaposing individual and group mobility. The framework was tested on auniversity campus that provides representative human mobility patterns with diverse attributes. Two tests that aim to demonstrate the framework’s capability to (1) capture individual mobility patterns by using data processing amenable to the spatiotemporal sparseness and to formulate group mobility and (2) differentiate quantitatively the spatiotemporal signatures of distributions, night activities, transitions, and network topologies were conducted. This study uncovers distinct disparities between individuals and groups, and heterogeneities among different attributes in both an empirical and real-world scenario with a reduction in computation time to approximately 6% of the baseline.

Gradient-Based Empirical Risk Minimization Using Local Polynomial Regression

In this paper, we consider the widely studied problem of empirical risk minimization (ERM) of strongly convex and smooth loss functions using iterative gradient-based methods. A major goal of the existing literature has been to compare different prototypical algorithms, such as batch gradient descent (GD) or stochastic gradient descent (SGD), by analyzing their rates of convergence to ϵ-approximate solutions with respect to the number of gradient computations, which is also known as the oracle complexity. For example, the oracle complexity of GD is [Formula: see text], where n is the number of training samples and p is the parameter space dimension. When n is large, this can be prohibitively expensive in practice, and SGD is preferred due to its oracle complexity of [Formula: see text]. Such standard analyses only utilize the smoothness of the loss function in the parameter being optimized. In contrast, we demonstrate that when the loss function is smooth in the data, we can learn the oracle at every iteration and beat the oracle complexities of GD, SGD, and their variants in important regimes. Specifically, at every iteration, our proposed algorithm, Local Polynomial Interpolation-based Gradient Descent (LPI-GD), first performs local polynomial regression with a virtual batch of data points to learn the gradient of the loss function and then estimates the true gradient of the ERM objective function. We establish that the oracle complexity of LPI-GD is [Formula: see text], where d is the data space dimension, and the gradient of the loss function is assumed to belong to an η-Hölder class with respect to the data. Our proof extends the analysis of local polynomial regression in nonparametric statistics to provide supremum norm guarantees for interpolation in multivariate settings and also exploits tools from the inexact GD literature. Unlike the complexities of GD and SGD, the complexity of our method depends on d. However, our algorithm outperforms GD, SGD, and their variants in oracle complexity for a broad range of settings where d is small relative to n. For example, with typical loss functions (such as squared or cross-entropy loss), when [Formula: see text] for any [Formula: see text] and [Formula: see text] is at the statistical limit, our method can be made to require [Formula: see text] oracle calls for any [Formula: see text], while SGD and GD require [Formula: see text] and [Formula: see text] oracle calls, respectively. Funding: This work was supported in part by the Office of Naval Research [Grant N000142012394], in part by the Army Research Office [Multidisciplinary University Research Initiative Grant W911NF-19-1-0217], and in part by the National Science Foundation [Transdisciplinary Research In Principles Of Data Science, Foundations of Data Science].

Professional Standards and Quality Concern in Teacher Education

The National Education Policy 2020 (NEP 2020) of India aims to revamp the education system and lay a roadmap for a progressive educated country. The policy recommends moving the teacher education system into multidisciplinary colleges and universities, with a four-year integrated B.Ed. program becoming the minimum degree qualification for school teachers by 2030. A common guiding set of National Professional Standards for Teachers (NPST) will be developed by 2022. The National Education Policy 2020 in India aims to improve the quality of teacher education by addressing quality concerns such as outdated curriculum, lack of subject-specific training, and ineffective teaching methods. Key provisions include a four-year integrated teacher education program, standardized teacher eligibility tests, and technology integration. To successfully implement the policy, collaboration between the government, teacher education institutions, and stakeholders is essential. Key reforms include ongoing continuing professional development, strengthening accreditation processes, developing mentorship programs, and promoting global exposure.

Factors Influencing University Student Satisfaction with Online Learning in Vietnam

Student satisfaction with online learning is critical for universities in developing countries. However, little is known on the impact of contextual and personal factors on the engagement and satisfaction of university students in developing countries with online learning. This study conducted a survey research to explore the factors determining on university student satisfaction with online leanring in Vietnam. It examines a sample of 427 undergraduate students at a leading multidisciplinary university who have experienced online learning for their university courses since early 2020. The structural equation modelling analyses using Smart PLS indicated that online learning service quality and student self-efficacy had significant relationships with student engagement which led to student satisfaction. In the online education context of Vietnam, although online learning service quality and student self-efficacy had effects on both behavioural, cognitive and emotional engagement, only behavioural and cognitive engagement had significant relationships with student satisfaction. The findings provides implications for educators in Vietnam to enhance the effectiveness of online learning.

Linguo-Pedagogical Design of Open Question Items in the Context of Digitalization

Interdisciplinary approach to the development and creation of open question items for assessment procedures in accordance with the digital transformation strategy and the new requirements for state accreditation of educational programs of higher education has been presented in the article. It has been revealed the content of the author’s terms including linguo-pedagogical mastery, linguo-pedagogical design, linguistic correctness and methodological literacy of test tasks. On the basis of scientometric analysis carried out due to the scientific electronic library elibrary it has been determined the chronological activity and subject correlation of publications of native researchers on testing students. Based on the results of a survey of teachers from 11 regional universities the main difficulties in the preparation of testing and assessment materials are summarized. Examples of test items in disciplines taught at a multidisciplinary polytechnic university are analyzed. Typical errors in the wording of questions, reference answers and evaluation criteria have been identified by the methods of erratological analysis and the reasons for their appearance have been summarized. To model the linguo-pedagogical design of test tasks the concept of an epistemic situation is used, its four components are described: ontological (subject-content definition of objects and elements of control), methodological (selection of types and types of tasks depending on the level of measured outcomes of learning), communicative-pragmatic (ensuring the comprehensibility of the text of the test task) and technological (technical implementation of the automated check of free response). Methods of test tasks modification with the help of modern digital services are proposed. As a result of the study, the authors formulate recommendations for the implementation of the linguo-pedagogical design of open question items with a focus on their implementation in the digital environment.

Smart Investigation Through Machine Learning: Study On Faculty Selection In A Large Multi-Disciplinary University Of North-Eastern India

Smart Investigation Through Machine Learning: Study On Faculty Selection In A Large Multi-Disciplinary University Of North-Eastern India

The perceptions of university students on technological and ethical risks of using robots in long-term care homes.

Introduction: The COVID-19 pandemic has disproportionately impacted long-term care (LTC) residents and exacerbated residents' risks of social isolation and loneliness. The unmet emotional needs of residents in LTC have driven researchers and decision-makers to consider novel technologies to improve care and quality of life for residents. Ageist stereotypes have contributed to the underuse of technologies by the older population. Telepresence robots have been found to be easy to use and do not require older adults to learn how to operate the robot but are remotely controlled by family members. The study aimed to understand the perspectives of multidisciplinary university students, including healthcare students, on using telepresence robots in LTC homes. The study would contribute to the future planning, implementation, and design of robotics in LTC. Methods: Between December 2021 and March 2022, our team conducted interviews with 15 multidisciplinary students. We employed a qualitative descriptive (QD) approach with semi-structured interview methods. Our study aimed to understand the perspectives of university students (under the age of 40) on using telepresence robots in LTC homes. Participants were invited to spend 15 min remotely driving a telepresence robot prior to the interview. A diverse team of young researchers and older adults (patient and family partners) conducted reflexive thematic analysis. Results: Six themes were identified: Robots as supplementary interaction; privacy, confidentiality, and physical harm; increased mental well-being and opportunities for interactions; intergenerational perspectives add values; staffing capacity; environmental and cultural factors influence acceptance. Conclusion: We identified a diverse range of perspectives regarding risk and privacy among participants regarding the implementation of telepresence robots in long-term care. Participants shared the importance of the voice of the resident and their own for creating more equitable decision-making and advocating for including this type of technology within LTC. Our study would contribute to the future planning, implementation, and design of robotics in LTC.

The Holodeck model, learning environments, and resilience in higher education

This paper proposes a flexible framework for developing asynchronous online learning environments for multidisciplinary contexts at an Australian university. The project addresses the need to build organisational resilience following the fragility demonstrated by educational institutions during the emergency transition to online learning in 2020. The paper examines the challenges experienced by staff and students, the future implications and the potential of the Holodeck model of a learning environment in circumventing these barriers. The analysis of the strategies implemented through various educational technologies in criminology, teacher education, international relations and research methodology, demonstrated its applicability in diverse academic fields. The paper promotes the capacity of the Holodeck-based framework to provide systematic support structures for university online and on-campus programs, and, consequently, to develop institutional resilience in multidisciplinary university contexts. The paper also outlines the next phase of the project, focusing on a student perspective of the use of the Holodeck model.

The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization

Online platforms have a wealth of data, run countless experiments, and use industrial-scale algorithms to optimize user experience. Despite this, many users seem to regret the time they spend on these platforms. One possible explanation is that incentives are misaligned: platforms are not optimizing for user happiness. We suggest the problem runs deeper, transcending the specific incentives of any particular platform, and instead stems from a mistaken foundational assumption. To understand what users want, platforms look at what users do. This is a kind of revealed-preference assumption that is ubiquitous in the way user models are built. Yet research has demonstrated, and personal experience affirms, that we often make choices in the moment that are inconsistent with what we actually want. The behavioral economics and psychology literatures suggest, for example, that we can choose mindlessly or that we can be too myopic in our choices, behaviors that feel entirely familiar on online platforms. In this work, we develop a model of media consumption where users have inconsistent preferences. We consider a platform which wants to maximize user utility, but only observes behavioral data in the form of the user’s engagement. We show how our model of users’ preference inconsistencies produces phenomena that are familiar from everyday experience but difficult to capture in traditional user interaction models. These phenomena include users who have long sessions on a platform but derive very little utility from it, and platform changes that steadily raise user engagement before abruptly causing users to go “cold turkey” and quit. A key ingredient in our model is a formulation for how platforms determine what to show users: they optimize over a large set of potential content (the content manifold) parametrized by underlying features of the content. Whether improving engagement improves user welfare depends on the direction of movement in the content manifold: For certain directions of change, increasing engagement makes users less happy, whereas in other directions on the same manifold, increasing engagement makes users happier. We provide a characterization of the structure of content manifolds for which increasing engagement fails to increase user utility. By linking these effects to abstractions of platform design choices, our model thus creates a theoretical framework and vocabulary in which to explore interactions between design, behavioral science, and social media. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was supported by the Vannevar Bush Faculty Fellowship and Multidisciplinary University Research Initiative [Grant W911NF-19-0217]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.03683 .

The impact of pedagogical and ICT training in teachers’ approaches to online teaching and use of digital tools

This study examines the relationship between teachers’ pedagogical and ICT training, their approaches to online teaching and the use of digital tools. The participants were 265, teachers from the large multi-disciplinary university in Finland. The data was collected in May–June 2020 during the early stages of COVID-19 pandemic. The results showed that teachers used digital tools more for delivering information and less for activating students. The study revealed that pedagogical training enhanced teachers’ learning-focused approach to online teaching and diverse use of digital tools. Contrary to expectations, teachers’ ICT training was not related to the use of digital tools. Therefore, it is important to provide pedagogical training to support teachers in implementing teaching in diverse teaching-learning environments and enhance building an online teaching, in which digital tools are used to promote interaction in online environment.

А model for the formation of a unified educational environment for students of pedagogical areas of training in a multidisciplinary university

А model for the formation of a unified educational environment for students of pedagogical areas of training in a multidisciplinary university

Visit Data and Telehealth in a Clinic for Trafficked Persons: Virtual Care and Human Trafficking During the COVID-19 Pandemic

Abstract: Background. Human trafficking involves coerced labor or sex. THRIVE, a multidisciplinary University of Miami clinic for trafficked persons, transitioned to a hybrid telehealth model during the COVID-19 pandemic. Th is paper presents appointment data across THRIVE clinic services during this transition. Methods. A review of appointments for THRIVE patients (n=156) was conducted to compare pre-pandemic (February 2019 to February 2020) and pandemic (March 2020 to December 2021) appointment trends. Results. There were 15% more scheduled (n=51.1 versus n=44) and 8% more completed (n=30.2 versus n=27.9) appointments per month early in the pandemic period with telehealth use compared to the pre-pandemic period. Telehealth was most used within psychiatry. Rescheduled and no-show appointments per month significantly increased during the pandemic period (p=0.010 in pandemic period 1 and and p=0.028 in pandemic period 2). There were few significant differences in appointment trends according to demographic variables. Conclusion. Telehealth succeeded in connecting THRIVE patients during the pandemic, highlighting its potential for long-term use amongst trafficked persons.

Assessment Literacy, Current Assessment Practices and Future Training: Reflections of Teachers in Higher Education

The aim of this study is to explore assessment literacy, to analyse the current assessment practices of teachers in Higher Education (HE) and to understand the need for alternative assessment practices in light of teachers’ reflections. The research employed incidental sampling to select (n=58) HE teachers from a single multidisciplinary university in Maharashtra State, India. A comprehensive literature review on assessment literacy provided the theoretical foundation, and a specialized assessment tool was developed to assess the teachers' assessment literacy level, perspectives, and practices in assessment. Data were collected through voluntary participation and analysed using quantitative and qualitative techniques. The findings revealed that the majority of HE teachers (79%) demonstrated a moderate level of assessment literacy, while 16% exhibited low levels and only 5% showed high levels. Additionally, a discrepancy was observed between the preferred assessment types of teachers and learners, with learners emphasizing active participation and real-world applications, while teachers leaned toward traditional evaluation methods. Participants were found to be literate only in specific aspects such as meaning, basic forms of assessments, and the purpose of educational assessments. Furthermore, teachers minimally acknowledge and have a lesser preference for alternative assessment methods, such as article analysis, team projects, case studies, and discussions. Overall findings indicate teachers' lack of knowledge about assessment practices that involve evaluating student performance, fostering collaboration, assessing higher-order thinking skills, and utilizing assessment as a tool for learning and improvement. This research confirms the need for training to enhance teachers' assessment literacy, promoting the adoption of alternative assessment practices in higher education to optimize student learning experiences.