Ungrading: The Case for Abandoning Institutionalized Assessment Protocols and Improving Pedagogical Strategies

Abstract

Our current grading system assumes everyone starts at approximately the same place. This assumption presumes uniformity and impartiality to be inherent in our school system. We argue that this is not the case. This work explores the evolution of grading systems and the integration of new technologies in education, focusing on the development of more inclusive, dynamic, and adaptable teaching and assessment strategies. Key methods include diversified assessments, experiential learning approaches such as problem-based learning and the generated question learning model, and the incorporation of artificial intelligence (AI) in hyperflex learning strategies. The proposed work astutely identifies the critical flaws within the modern grading system and puts forth a compelling solution: shifting the focus towards assessing students’ improvement scores. This approach not only offers a progressive path forward, but also significantly enhances equity by holding students accountable for their knowledge gaps while promoting a more comprehensive evaluation. Additionally, creative engagement techniques, such as mock banking reward systems, are employed to enhance student motivation and participation. AI-facilitated formative assessments and personalized learning plans are also discussed, emphasizing the importance of real-time insights into student progress and the provision of flexible personalized learning environments. This comprehensive approach to education fosters student ownership of learning, promotes active participation, and equips students with essential lifelong learning skills. Moreover, a more accurate assessment of student learning and progress would be fostered, thus creating a paradigm shift from the currently flawed grading system.

1. Introduction

The assumption that uniformity and impartiality are inherent in the grading system is a fallacy. Why do we say this? The following scenario offers a preliminary view of the reasons for the assertion. A teacher gives a test to a class of, say, 30 students and assigns grades based on the number of correct responses. The teacher’s grading is predicated on the belief that all students have had similar understanding of and access to the materials presented; thus, the test and grading system are fair. While this traditional assessment method may seem fair on the surface, it is built on deficit thinking. The hallmark of this type of thought is that grades must be distributed over a normal curve, expecting the inevitable failure of some students. The expectation of a normal distribution with forecasted failure leads to educators absolving themselves of responsibility for student failure. However, by shifting away from institutionalized grading systems and adopting strategies, such as portfolio assessment, contract grading, and narrative evaluations, educators can be empowered to create a more equitable and supportive learning environment [1].

Moreover, when educators focus on formative feedback, self-assessment, and reflection, they foster a growth mindset and enhance student empowerment and ownership of their learning [1]. Educators are especially positioned to challenge deficit thinking, prioritize the development of the whole student, and ultimately create more inclusive and effective education systems. Case in point: The 1988 American drama film, “Stand and Deliver”, dramatized the true story of one mathematics teacher from James A. Garfied High School in East Los Angeles, Jaime Escalante, who employed novel teaching and assessment strategies to turn around and transform the dismal learning and failing trajectory of his students into successful learning outcomes in calculus [2]. The Library of Congress logged this drama film in The United States National Film Registry in 2011, a testament to how educators can and must challenge deficit thinking, prioritize the development of the whole student, and ultimately create a more inclusive and effective education system.

The research aims to investigate the utility and impact of incorporating improvement scores as an assessment metric in evaluating students’ performance. The objective is to assess whether the inclusion of improvement scores provides a more comprehensive understanding of student progress and contributes to a fairer and more accurate evaluation system. Through quantitative analysis and comparison with traditional assessment methods, the research seeks to determine the effectiveness of using improvement scores and their potential implications for educational assessment practices.

1.1. A Brief History of Grading

Grading practices, such as awarding letter grades, using evaluations or numerical grades, and grading on a curve, are all relatively new concepts in education. Ancient school systems were based on the realization that there are multiplicative factors involved in bringing about outcomes and the desire to want to know. One could conceive of it as a journey akin to Socrates taking the individual from the cave into the world of light. Thus, success, as demonstrated by Socrates, would be achieved by awakening hidden knowledge. Socrates believed that truth was always in the soul and that, with a series of questions, one could lead an individual to truth. Moreover, in ancient Greece, assessments were formative rather than evaluative. That is, early grading was not predicated on assigning a number to an individual’s progress. Rather, all learning was through some form of apprenticeship. Thus, the historical understanding of learning was that it was best achieved experientially, meaning transferable skills were imparted through guided activities formed within experiences. Further, meaningful marks or symbols were used to denote achievement in certain areas. For example, in ancient Greece, athletes who won events in the Olympic Games were given a wreath made of leaves as a symbol of their achievement. In medieval Europe, craftsmen who had completed their apprenticeships were given a “master’s mark” to indicate their skill level [3].

Additionally, oral examinations were used to assess knowledge and understanding. In ancient China, for example, civil service examinations were used to determine the suitability of candidates for government positions. Similarly, in medieval Europe, oral examinations were used to test the knowledge of candidates for ordination as priests [3]. It was not until the 18th century that formal grading systems began to emerge in Western educational institutions. What was the thought behind such a paradigm shift? Did our current grading system arise from expedience and impatience with allowing learners to individually base timeframes for exploration to arrive at an independent conclusion? It seemed that the need for complementary conclusions, endorsed as accepted knowledge, necessitated an abridged process, focusing on answering correctly while stifling the possibility of exploration. As a result, a model that accounted for “right answers” the mind could recall had to be devised. One early example was the system developed by William Farish, a professor at the University of Cambridge, in the late 18th century. Farish’s system assigned numerical values to student performance, with higher numbers indicating better performance [4].

These methodologically new developments were followed by a progression of institutionalized assessment protocols. In 1646, Harvard instituted exit exams as a requirement to attain a degree. More than a hundred years later, in 1785, Yale’s president, Ezra Stiles, implemented the first grading scale in the United States based on four descriptions: Optimi, Second Optimi, Inferiores, and Perjores. Other universities, like William and Mary, enacted similar approaches circa 1817 [5]. The emergence of these grading systems was closely associated with the United Kingdom, as researchers have deduced that educators, like Stiles, had begun imitating a classification structure prominently displayed in the Cambridge Mathematical Tripos examination designed to evaluate student learning [6]. Ultimately, these systems became a global phenomenon.

However, pedagogical figures such as Horace Mann were apprehensive about the inherent message of competition communicated to students through grading systems and their potential effects on student learning and intellectual development. In his ninth annual report, Mann noted, “if superior rank at recitation be the object, then, as soon as that superiority is obtained, the spring of desire and of effort for that occasion relaxes,” adding that students might prioritize exam outcomes “as to incur moral hazards and delinquencies” [7]. The debate about the merits of grading has existed since its inception. Even so, grading moved from its holistic origins to a more standardized, purportedly objective, and scale-based approach in the early 1900s as U.S. education expanded and tripled in size due largely to compulsory K-12 education. Consequently, a unified system that prioritized standardized and efficient communication between academic institutions emerged. Grades could no longer be specific to an individual school, university, or student but needed to have meaning to third parties. From then on, the grading system, as we know it, became widespread across education.

By the 1940s, the A–F grading system had became “the dominant grading scheme, along with two other systems that would eventually be fused together with it: the 4.0 scale and the 100 percent system” [6]. The history of grading in schools has been a complex one, with roots in the industrial era and the need to sort and stratify students for future employment [8,9]. This context has continued to influence the way grades are assigned and interpreted, albeit not necessarily in beneficial ways, with many traditional grading practices relying on subjective and biased judgments that can lead to inequitable outcomes for students. For example, research has shown that Black and Latinx students are more likely to receive lower grades than their White peers, even when they have similar academic performance [8,9].

Grading systems have since evolved and become more complex, with a variety of different scales and methods used to assess student achievement. However, there is an ongoing debate about the effectiveness and fairness of grading, with some critics arguing that it can be a poor indicator of actual learning and that it can unfairly advantage certain students over others [4] (Kohn, 2011). The goal of this article is to offer a comprehensive exploration of the issue, providing a framework for creating more equitable grading practices that can support all students in achieving success. In this theoretical study, we explore reasons for a new grading system and propose a more innovative approach to grading.

1.2. A Modern Grading System in the Image of Capitalism

The modern grading system, which evaluates and categorizes students based on their academic performance, can be connected to capitalist ideologies through various aspects, such as meritocracy, hierarchical structure, individualism, standardization, and a market-driven approach. Capitalism is an economic system characterized by private ownership of resources, competitive markets, and the pursuit of profit. It thrives on individualism, meritocracy, and the idea that hard work leads to success [10].

In this context, the grading system shares some of these core principles and can be seen as a reflection of capitalist values within the educational sector [4]. Just as capitalism rewards those who demonstrate skill, innovation, and hard work, the grading system rewards students based on their performance, promoting competition and encouraging them to work hard to achieve success [11]. In classrooms across every age level, students are encouraged to outperform their peers by answering questions unknown to them [12]. Moreover, it is believed that competition stimulates excellence and creates a context that fosters progress at rapid rates [12].

Additionally, both capitalism and the grading system create hierarchies based on wealth and academic performance, respectively, which can contribute to perpetuating inequalities in education and beyond [13]. Capitalist ideologies also emphasize the importance of individual effort and ambition, which is mirrored in the grading system by evaluating students on their individual performance, often disregarding external factors that may impact their ability to succeed academically [4]. This focus on individual achievement can lead to a neglect of systemic issues affecting education, such as poverty, access to resources, and cultural factors [14].

Furthermore, capitalism relies on the production and exchange of goods and services in a standardized manner [10]. The modern grading system often reduces learning and knowledge acquisition to a set of quantifiable and standardized measures, such as test scores and grades [15]. This commodification of education can limit creativity, critical thinking, and intellectual curiosity, as students may focus solely on achieving high grades rather than truly engaging with the material [16].

Finally, grading systems can be influenced by market demands, as educational institutions may prioritize subjects and skills that are deemed economically valuable [17]. This market-driven approach can lead to a narrow focus on STEM subjects and vocational training, potentially marginalizing the arts, humanities, and other fields that may not have immediate economic benefits [18].

By understanding these connections between the modern grading system and capitalist ideology, educators and policymakers can work towards addressing the potential drawbacks of the modern grading system and promoting a more equitable and inclusive educational experience for all students [19].

1.3. Current Grading System Impact on Students

Grading practices in schools have been a long-standing controversial topic, with many arguing that traditional grading systems perpetuate inequities and create barriers to success for students from marginalized communities. Traditional grading practices, such as assigning points to individual assignments or averaging scores across different types of assessments, can be subjective, biased, and fail to accurately represent students’ learning and growth. Research has presented evidence that students who are most negatively affected by traditional grading practices tend to be those who are already marginalized, such as low-income students, students of color, and English language learners [8]. Notwithstanding, the modern grading system can have both positive and negative impacts on a student’s emotional state (see

Table 1. Potential Effects of an Established Grading System.

Impact	Description
Motivation Goal-Setting	Grading systems encourage students to set goals and work towards them, potentially increasing motivation and sense of accomplishment.
Competition and Comparison	Grading systems foster a competitive environment, which can be motivating for some but cause stress or feelings of inadequacy for others.
Stress and Anxiety	The pressure to achieve high grades can lead to stress, anxiety, and potential mental health issues, such as depression.
Self-esteem and Self-worth	Academic performance can impact self-esteem, with high achievers experiencing a boost and low achievers potentially suffering from low self-esteem.
Fear of Failure	Modern grading systems can create fear of failure, causing increased stress, anxiety, or avoidance of challenging tasks.
Creativity and Innovation	Narrow assessment criteria may stifle creativity and innovation, limiting students’ willingness to take risks or explore interests outside the curriculum.

).

As outlined in Table 1, a modern grading system can have a range of impacts on a student’s emotional state, including motivation, stress, anxiety, self-esteem, fear of failure, and creativity. These effects vary depending on the individual student’s personality, support system, and educational environment. For example, limited resources and support, coupled with the intersections of low socioeconomic status and race, can have compounded negative consequences for student learning and grades within the context of the modern grading system. Further, low socioeconomic backgrounds reciprocally interact with several factors, including reduced parental involvement, health and nutrition, economic instability, crime, and limited access to educational materials, such as textbooks, technology, or internet access, which can hinder their learning experience and lead to lower grades [20,21,22,23].

Parental involvement, which is crucial for student achievement, may be less than optimal in low-income families due to limited time or resources for engaging in their children’s education [22]. Moreover, low-resource students may experience health issues or inadequate nutrition, which can impede cognitive development, academic performance, and, consequently, grades [21]. When increased levels of stress due to financial instability or neighborhood crime are added in, there is further impact on their ability to focus, learn effectively, and achieve high grades in the modern grading system [20]. Furthermore, within their ecological contexts, low-resourced students may not have access to a conducive learning environment inclusive of a quiet study space or proper lighting, which can negatively affect their learning and academic performance [24]. Finally, there are broader structural issues faced by low-resourced students, including school systems that struggle to attract and retain high-quality teachers, schools with larger class sizes, and limited resources, all of which can negatively impact student learning outcomes and contribute to lower grades within the context of the modern grading system [25].

Thus, modern grading is embedded in a system and mindset of deficit thinking that force students with variable beginnings to compete unfavorably with one another. It is designed to single out the best and award them while undervaluing those whose scores fail to meet standards. This system provides a false sense of attainment and accomplishment, as it does not predict student potential but would most likely stifle it. Within this system, it is common for students who are categorized as A students to have self-doubt when they do not obtain grades consistent with their previous performances, often with no room for self-reflection on their part. The converse is true for students who are consistently categorized as C or D students to doubt themselves even when they obtain higher grades in their classes. Fundamentally, the modern grading system, as noted previously, is grounded in the assumption of a normal curve, such that there is not an explicit expectation for every educator to ensure that their instructional pedagogies facilitate performance in the top tier. Consequently, educators are not encouraged to be accountable for their instructional and evaluative methods for all students.

1.4. More Equitable Practices and the Era of AI

To address the aforementioned issues, a framework for grading that emphasizes clarity, consistency, and transparency is needed. This includes the use of clear and specific grading criteria, meaningful feedback that can support student learning and growth, and opportunities for students to demonstrate their knowledge and skills in multiple ways. By using these practices, Feldman [8] has argued that educators can create grading systems that are more equitable and supportive of success for all students [8,26]. Redefining current evaluative methods has become crucial given the advent of AI, whose impact on education requires that we implement practices that are more personal and impartial.

An antidote to the array of issues inherent in modern grading is experiential learning, an educational approach that emphasizes active participation, engagement, and reflection, allowing students to learn through experience. Although this approach offers numerous benefits, it can sometimes be challenging to integrate into traditional grading systems that primarily rely on standardized assessments and quantitative measures. To incorporate experiential learning into modern grading systems, educators can develop alternative assessment methods like project-based assessments, presentations, and portfolios, as well as focus on skill development, such as collaboration, problem-solving, and communication. Additionally, important to this approach is encouraging self-assessment and reflection, which promote a growth mindset and help students become more self-aware [12].

One key aspect of equitable grading approaches is the use of clear and specific grading criteria that would replace the reliance on vague or subjective standards. Educators, in this framework, can develop rubrics and other tools that clearly outline the criteria students must fulfill to earn a certain grade. This can help eliminate bias and ensure that students are evaluated based on their actual performance rather than on other factors, such as race, gender, socioeconomic status, or their intersections [8,27]. Another important component of this framework is the use of meaningful feedback. Rather than simply assigning grades without explanation, educators can provide specific and measurable feedback that thoughtfully outlines what students did well, where they need to improve, and how they can continue to develop their skills and knowledge. This feedback can be especially important for students from marginalized communities who may not have access to other sources of support and guidance [28,29].

Additionally, experiential learning in an equitable grading system involves designing learning experiences that accommodate the diverse needs, backgrounds, and abilities of all students. The main objective is to ensure that all students have a fair opportunity to succeed, irrespective of their starting points. This type of learning emphasizes hands-on experiences, real-world applications, and active engagement in the learning process (see

Table 2. Elements of Experiential Learning in an Equitable Grading System.

Key Element	Description
1. Differentiated Instruction	Addresses varied learning styles, strengths, and challenges by offering multiple formats for assignments and being responsive to students’ interests [30,31].
2. Clear Expectations and Rubrics	Develop transparent, objective learning outcomes and assessment criteria focused on the learning process, growth, and mastery of skills that are shared with students [32,33].
3. Formative Assessment	Regularly assess student progress using methods like self-assessment, peer feedback, and teacher observations to allow for ongoing adjustments to instruction and support [34,35].
4. Collaborative Learning	Encourage group work and collaborative learning activities to foster community, cooperation, and shared responsibility for success [30].
5. Reflection	Incorporate reflection activities, such as journaling, discussions, or presentations, to facilitate the development of self-awareness, critical thinking, and problem-solving skills [8].
6. Culturally Responsive Teaching	Recognize and value students’ cultural, linguistic, and experiential diversity, incorporating their backgrounds into the curriculum, and creating inclusive learning environments that validate and affirm all students [1,8,30].
7. Access to Resources	Ensure equal access to resources and materials necessary for learning experiences, providing additional support or accommodations for students with intersectional diversity dimensions of disabilities or socio-economic challenges [1,8].

for a summary of key elements).

By integrating the elements described in Table 2, experiential learning in an equitable grading system will create a more inclusive and supportive learning environment that promotes the success and growth of all students [30]. While the framework proposed by Feldman is not without its challenges, it offers a powerful approach to implementing more equitable grading practices in schools. As previously indicated, through an emphasis on clarity, consistency, and transparency, educators can design grading systems that are more objective and less prone to bias while also providing students with the support and guidance they need to achieve success [36,37].

Despite its benefits, one potential criticism of Feldman’s [8] approach is that it focuses primarily on the K-12 education system in the United States, which may limit its applicability to educators in other contexts or regions. Additionally, some may find the emphasis on grading systems to be overly narrow, as there are other important contributing factors to student success beyond grades, such as social and emotional learning. These aspects also require attention to create more equitable and supportive learning environments [38,39].

Notwithstanding its limitations, the framework presented by Feldman [8] offers a valuable starting point for educators desirous of creating more equitable grading practices in their schools and classrooms. To accomplish this, educators need to focus on clarity, consistency, and transparency. When these elements are integrated, educators can work to eliminate bias and create systems that support the success of all students. In addition to the previous elements, giving emphasis to growth-inducing feedback and using multiple forms of assessment can assist educators in the creation of learning environments that privilege inclusivity and respect for diverse strengths and abilities of all students.

Beyond the dimensions described above, AI can be employed to facilitate experiential learning in various ways, including personalized learning, real-time feedback, simulations and virtual environments, and collaboration and peer review [40]. AI algorithms can analyze students’ learning patterns and preferences, tailoring learning experiences to meet their individual needs. Moreover, they can provide immediate feedback and guidance to students during experiential learning activities, helping them adjust and improve in real-time.

As outlined in

Table 3. Use of AI Tools and Experiential Learning.

Impact	Description
Enhanced Learning Resources	AI tools, such as, ChatGPT can provide instant feedback and explanations, potentially improving students’ understanding and grades [36,41].
Plagiarism Concerns	AI tools-generated content raises concerns about plagiarism and originality, requiring the use of AI detection tools in grading [37,38,42,43].
Study Aid	Students can use AI tools as a study aid, but over-reliance may hinder critical thinking and independent problem-solving development [39,40,44,45].
Automated Grading Assistance	AI tools like ChatGPT can assist in grading objective tasks, potentially increasing consistency, and efficiency in grading practices [41,46].
Personalized Feedback	ChatGPT can generate personalized feedback for students, potentially contributing to improved learning outcomes and targeted growth [41,46].
Changing Evaluation Methods	The use of AI tools like ChatGPT may necessitate reevaluation of current grading methods and development of new assessment strategies [42,47].

, AI tools such as ChatGPT or Tallie have the potential to impact modern grading systems in various ways, including enhanced learning resources, plagiarism detection, study aid, automated grading assistance, personalized feedback, and the integration of evolving evaluation methods. The overall effect on grading systems will depend on how educational institutions and students adapt to and utilize AI tools in the learning process. These tools have the potential to be more inclusive and adaptive because they offer an array of hands-on or task-based learning with varied opportunities to assess skills imparted to the learner.

It is important to note that in experiential learning grading or evaluation, educators should consider both the learning process and the outcomes. Methods like rubrics, self-assessment and reflection, peer assessment, portfolios, and multi-dimensional grading, when used, can create a more comprehensive grading system that captures the full range of learning experiences [48]. Ultimately, the best method for grading experiential learning is dependent on the specific goals of the educational program and the learning objectives of individual students. By prioritizing flexibility, adaptability, and a focus on personal growth, educators can create grading systems that better align with the principles of experiential learning.

In conclusion, the issue of grading practices in schools is a complex and contentious one, with many arguing that traditional systems perpetuate inequities and create barriers to success for students from marginalized communities. Integrating the positive use of AI to create an equitable grading system combined with experiential learning can potentially alleviate some of these concerns. As Feldman [8] has argued, there are ways to create more equitable and supportive grading practices that can benefit all students. Educators can work to create systems that support the success of all students, regardless of their background or circumstances, which can be achieved by emphasizing clarity, consistency, and transparency coupled with providing meaningful feedback and opportunities for diverse forms of assessment. The integration of AI tools can further enhance this process, providing personalized feedback and targeted support to students from various backgrounds. Furthermore, experiential learning can help bridge the gap between students from different socioeconomic backgrounds by emphasizing hands-on, real-world experiences that promote engagement and deeper understanding. As such, “Grading for Equity” offers a valuable resource for educators looking to create more equitable and inclusive learning environments that support the success of all students, with the potential assistance of AI and experiential learning approaches.

1.5. Personalized Learning: Leveraging Baseline Assessments to Enhance Student Success

Educators are always intent on discovering innovative methods to improve student performance and engagement. One revolutionary approach has been inspired by the work of William H. Crogman, a pioneering African American educator, who advocated for personalized learning to enhance student success [49,50]. Personalized learning is an educational approach that customizes instruction to meet the unique needs, interests, and abilities of individual learners [51]. An effective approach to personalizing learning that enhances student success is baseline assessments, which are diagnostic tools used to gauge students’ existing knowledge, understanding, and skills in a particular subject area [52]. These assessments provide valuable information about the strengths and weaknesses of individual students, enabling educators to tailor their pedagogical approaches accordingly. Essentially, baseline assessments provide a clearer understanding of each student’s initial knowledge and skills, inspiring instructors to develop targeted teaching strategies that address the specific needs of their learners. For example, within the context of physics education, instructors who use baseline assessments, such as the Force Concept Inventory (FCI) can measure students’ understanding of fundamental concepts like force and motion [53]. Informed by this information, they can adapt or adjust pedagogical strategies to accommodate the diverse abilities of their students. The effectiveness of FCI in improving student learning is well established [30,53].

Essentially, the three important constructs and elements in personalize learning are:

• Targeted Instruction: Personalized learning allows instructors to identify areas where students require additional support or enrichment. By recognizing the specific needs of each learner, educators can develop targeted instructional strategies that are responsive to the diverse abilities of their students [51].
• Enhanced Engagement: When students are provided with learning opportunities suited to their individual needs, they are more likely to be engaged and motivated to succeed. By using baseline data to inform instruction, teachers can create a more inclusive and dynamic learning environment that fosters student success [49].
• Measuring Growth and Improvement: Baseline assessments also offer a valuable means of tracking student progress over time. By comparing initial scores with those obtained later in the course, educators can quantify student improvement and determine the effectiveness of their teaching strategies [52]. Moreover, this process ensures that students are serving as their own control, contrasting their growth over time rather than comparisons with other students with variable backgrounds and contexts.

There are important strategies for implementing personalized learning in the classroom, which include the following planned process. Please note that the process begins with baseline evaluations.

• Administer the Baseline Assessment CI Early in the Course: To ensure accurate baseline data, it is crucial to administer CI at the beginning of the course before any formal instruction has occurred. This will provide instructors with a clear snapshot of students’ existing knowledge and understanding of the subject matter [30].
• Analyze the Data: Once the CI baseline assessment has been administered, educators should carefully analyze the results to identify patterns and trends among their students. This analysis can help instructors pinpoint specific areas where students are struggling and develop targeted interventions to address these gaps in understanding [51].
• Differentiate Instruction: Informed by the baseline data, educators can design a pedagogy that addresses the unique needs of their students. This may involve creating tiered assignments, offering additional support for struggling learners, or providing enrichment opportunities for more advanced students [51,54].
• Monitor Progress and Adjust Instruction: Throughout the course, instructors should continue to track student progress by administering additional assessments and comparing the results to the initial baseline data. This ongoing process of monitoring and adjusting instruction will ensure that students receive the support they need to succeed in the course [37,43,49,55].

Incorporating baseline assessments such as the CI in introductory courses (since many of these courses are gateway courses) can significantly improve student success by enabling educators to develop targeted, data-driven instructional strategies. By identifying and addressing the unique needs of each learner, instructors can create a more inclusive and engaging learning environment that fosters academic achievement. The above information has explored the benefits of baseline assessments and offered suggestions for their effective implementation in the classroom.

2. Method and Materials

Participants: A preliminary trial pilot was conducted on a group of 40 students in a general physics class at a local Californian university. The group was primarily of Hispanic descent, with an average age of 22.5 years old, majority male.

Tools: We administered the Force Concept Inventory (FCI), as a pre-test, aiming to establish a baseline understanding of force concepts and post-test, distinct from the FCI but designed to assess the same force concepts within the class.

Design: This was a pre-post test instructional pilot design. Following the baseline assessment, focused instructional sessions on Force were delivered over the span of a semester. To gauge the efficacy of the instruction a post-test was subsequently administered.

Procedure: Both tests were conducted under standardized conditions in a controlled classroom setting. The pre-FCI was delivered the first day of instruction. Sessions on Force were delivered over two weeks period, followed by a post-FCI at the end. Upon completion, student improvement scores were computed by plugging pre-test and post-test results.

Once an examination of improvements has been completed, correlate the improvement scores to a 0 to 30 grading scale. This can be accomplished by using a simple linear transformation. First, determine the range, which is minimum and maximum improvement values in your dataset. In the result, the minimum improvement is −3.5 and the maximum improvement is 16.5. Next, use the formula for linear transformation to convert the improvement scores to a 0 to 30 grading scale: Let the new score be $x$, therefore,

$$x=\frac{\left(x_0-x_{0, min}\right)}{\left(x_{0,max}-x_{0, min}\right)}\times \left(x_{max}-x_{min}\right)+x_{min}$$

However, in

Table 4. Students Test Scores for an Introductory Course.

Baseline Score	Actual Score	Improvement	New Score
29	25.5	−3.5	23.3
30	28.75	−1.25	28.5
25	24	−1	24.3
22	22.5	0.5	24.3
10	19.5	9.5	30
7	21	14	30
24	24.3	0.3	25.9
12	24	12	30
9	25.5	16.5	30
5	9	4	14.3
15	20.7	5.7	27.7
3	10.5	7.5	19.3
24	26.1	2.1	29.5
12	9	−3	7.3
13	14	1	23.3

Color-coding is used to emphasize notable trends in improvement scores. Green indicates that the student’s baseline is at the saturation limit, but the actual score is lower. Gray signifies that both the student’s baseline and actual scores are within the saturation limit, though the baseline is higher than the actual score. Brown denotes insufficient improvement, while blue represents an actual score that’s too low.

in result section, we use a much simple formular:

$$x=min\left(\{x=x_a if x_a\ge 27; \left( x_a+x_m\right)/2+stderr+\Delta x_i otherwise;, 30\right)$$

where $x_m=\{x_{mean}{ \mathrm{if} x}_{mean}\langle x_0{ \mathrm{or} x}_0{ \mathrm{if} x}_{mean}\rangle x_0$, $x_{mean}$ is mean of the baseline score, $x_a$ is actual score, stderr is standard error, $x_0$ is baseline score, and ${\Delta x}_i$ is the improvement. This quantitative metric was central to our analysis, allowing for the systematic study of students’ performance enhancements and highlighting the effectiveness of the instruction provided. Following the release of their scores, students were given a debriefing about the pilot and their progress. Further, modify the above formula to o include the difficulty measure (dm):

$$x=min\left(\{x=x_a if x_a\ge 27; \left(\left( x_a+x_m\right)/2+stderr+\Delta x_i\right)\times \left(1-dm/10\right) otherwise; , 30\right)$$

In this modified formula, the Difficulty Score is considered as a factor in determining the new score. The difficulty score is normalized to a scale from 0 to 1 by dividing by 10, ensuring it does not have an overly dominant influence. The greater the difficulty score, the smaller the multiplier, resulting in a lower new score when the test is perceived as more challenging.

By using the class average, it ensures that no student gets a zero as suggested Feldman [8]. In this line of reasoning, examinations are biased because they require students to make assumptions about the exact nature and content of the test. As such, there is a probability that their chosen study materials may match the information on the test. However, if the probability of what is studied is low for test content, it does not mean that the student did not learn anything. If that is the case, then it would suggest that the teacher’s pedagogy was not effective.

When measuring improvements in performance, the “Saturation Range” is an important construct. It refers to the achievement of a high-level proficiency as evidenced by test scores, making additional improvements increasingly difficult to attain or ascertain. For example, given a maximum possible score of 30, the saturation range would be defined as the set of scores within three points of this maximum. This means that any score from 27 to 30 (inclusive) would fall within the saturation range. Thus, when students receive scores within the saturation range, it is expected that their future scores will likely remain constant due to the challenge of obtaining further improvement. Essentially, performing within the saturation range suggests that students have nearly mastered the content being tested and that additional study or instruction may yield minimal increases in the test score or possible regression toward the mean.

3. Results: Grading Improvement: Concrete Example

In addition to integrating baseline assessments, it is crucial to effectively measure improvements in students’ performance. To demonstrate a process for creating more effective measurements of student performance improvement, consider the dataset of 40 students in Table 4 (Only 15 data point shown, data for all 40 student is given in Appendix A), which provides information on baseline scores, actual scores, and improvement measures. Note that the difference between the baseline score and the actual test scores for each student is calculated. This difference represents the amount of improvement students made between the baseline and actual test. In examining the improvement column, one can visually see which students made the most improvement between the baseline and actual test. Student improvement indices are important as they allow educators to evaluate the effectiveness of their instruction across all students [30,56,57,58,59].

Note that there was an inverse relationship between baseline and improvement scores. A higher baseline resulted in a smaller improvement score. A high baseline score represented better preparation, which translated into a higher probability of performing well, yet, at the same time, they were closer to the saturation state as far as improvement is concerned. Keep in mind, however, improvements within the saturation range are not an impossibility, but rather they are challenging and might require significantly more effort, different strategies, or focused refinement of specific skills. Student 2, color-coded gray in Table 4 exemplified saturation, had a high baseline score of 30 and a small negative improvement, but their actual score is above 1SD. In comparison, the student with the lowest baseline score of 3 had an improvement of 7.5, which represented a significant improvement. Similarly, the student whose baseline score was 9 improved by 16.5 points, which was another major improvement. These improvements of the students contrasted with the small improvement of only 0.5 for the student with a baseline score of 22, and the slight negative improvement for the student with a baseline score of 30 (see Table 4).

4. Discussion

Let us break down the information provided in Table 4, which has been color-coded for easier understanding. The data highlighted in green shows an improvement score of −3.5, which suggests that the instruction may have introduced some confusion rather than aiding understanding. We had expected students to fall within the saturation range after instruction, but this did not occur. This may be explained by instruction being particularly aimed at enhancing the learning potential of the weakest students. The situation, however, differed from the data represented by the second color code, where the instruction seemed to have had the desired effect.

Therefore, the data in the third and fifth color codes indicates a need for special instruction. These students are struggling to keep pace with the rest of the class. For the third color-coded student, although there was some improvement, it was insufficient. Implementing an online peer instruction method could potentially address this issue. The same approach would be beneficial for the student, represented by the fifth color code. In these instances, utilizing an AI tutor and peer assistance could provide additional support and enhance the learning process. Finally, the student represented by the fourth color code, despite showing significant improvement, still has a relatively low actual score. Therefore, assisted peer instruction would be crucial for further advancement.

Measuring student improvement individually in this manner provides valuable data for educators, which could help them reflect on the unique impact of pedagogical strategies. In addition to measuring individual improvements, computing overall data, as depicted in

Table 5. Comparison Data for Baseline Scores, Actual Scores, and New Scores.

	Baseline	Actual Score	New Score
Mean	16.2	20.7	24.2
SD	7.7	5.7	5.3
stderr	1.3	0.9	0.8

, provides educators with a composite picture of the group, which could help them evaluate strengths and areas for adjustments relating to their teaching and student engagement.

When considering data on student improvement, it also is important to consider normality and skewness. From Figure 1, we can see the normality of the data. Figure 1a showed that the baseline score was normally distributed, but the data in Figure 1b was not normally distributed (Shapiro–Wilk test for normality also confirmed the normality of the data).

The standard deviation is most meaningful when the data is normally distributed. If the data is not normally distributed, the standard deviation might not provide a meaningful measure of spread. It is important to remember that percentages (i.e., 68%, 95%, and 99.7%) associated with standard deviations in a normal distribution are not the actual scores themselves. Rather, they represent the proportion of data points that fall within a specific range. For this reason, scoring above 1SD is above the 84th percentile, and a student who scores a 27 is above 1SD and is within the saturation state.

In this study, we aimed to compare the performance of students with three different test scores: baseline, actual, and new scores (see Table 5). Our primary objective was to determine if there were any significant differences between the test scores and to discuss the potential implications of these differences for educational practices.

Let us analyze our data by comparing the various scores. The results revealed a statistically significant difference between the means of the test scores, F (2, 78) = 55.84, p = 0.00 (Greenhouse-Geisser corrected). The partial eta-squared (η²) of 0.35 indicated that approximately 34.8% of the total variance in the scores can be attributed to the within-subjects factor (the test scores). This finding suggested that the test scores have a meaningful impact on student performance.

We also calculated the effect sizes (Cohen’s d) for pairwise comparisons between the test scores. The effect sizes were as follows: Baseline vs. Actual: d = −0.49; Baseline vs. New: d = −1.75; Actual vs. New: d = −1.42. These effect sizes indicated moderate to large differences between the test scores, with the negative values suggesting that the actual and new scores have higher means than the baseline scores. This pattern of results might imply that the strategies, interventions, or assessment methods implemented in the actual and new scores conditions were more effective in enhancing student performance compared to the baseline condition. Future research could investigate the specific factors contributing to the observed differences, such as teaching methods, curriculum content, or assessment format.

Additionally, box-whisker plots for baseline scores, actual scores, and new scores were created. They suggested an overall improvement in test scores and a decrease in variability across test conditions as the IQR became slightly narrower for actual scores and new scores compared to baseline scores (Figure 2).

Although our study provides valuable insights, there are some limitations to consider. First, the relatively small sample size may limit the generalizability of our findings. Replicating the study with larger and more diverse samples would help to strengthen the evidence base. Second, the violation of the sphericity assumption (Greenhouse-Geisser epsilon value of 0.55) warrants caution in interpreting the results. Future studies should consider using alternative statistical methods or designs that do not rely on the sphericity assumption.

Nevertheless, the findings have important implications for educational practices. The observed differences between test scores suggest that certain approaches may be more effective in promoting student learning and achievement. Educators and policymakers should consider the factors that contributed to the higher performance in the actual and new scoring conditions when designing and implementing instructional strategies and assessment methods. Further, good instruction creates an outcome of low spread and low standard deviation, which means everyone is successful in the class. Thus, it is a capitalistic ideology that forces student outcomes to be normally distributed, with winners and losers. This is a system of failed instructions.

Improvement scores, which measure the delta between initial (pretest) and subsequent (post-test) assessments, have emerged as pivotal metrics in the educational landscape. Improvement scores, when leveraged in educational evaluation, can provide a multifaceted view of student learning, transcending the confines of static, singular performance indicators. As Hake (1998) [53] demonstrated through a comprehensive study of introductory physics courses, such metrics can offer invaluable insight into the nuanced progression of students, emphasizing both their starting points and the distance they’ve traversed academically. Individual learning trajectories, when understood through this lens, pave the way for more tailored and responsive teaching approaches catering to the diverse needs of learners [60]. Importantly, as Black and Wiliam (1998) noted, the feedback loops these scores provide can be instrumental for educators, guiding pedagogical adjustments and innovations [61]. Furthermore, improvement scores, by recognizing and celebrating growth, can serve as powerful motivational tools for learners [62]. On a broader scale, by identifying areas with limited growth, educators and curriculum developers can refine instructional content and strategies, ensuring optimal learning outcomes [63]. In sum, the embrace of improvement scores as central to educational assessment can play a pivotal role in not only understanding but also enhancing student learning.

Therefore, focusing on students’ improvement scores rather than comparing them to each other has several benefits and promotes a positive learning environment. By emphasizing individual progress, this approach removes unnecessary competition and comparison between students. Instead of students feeling pressured to outperform their peers, the focus is shifted to personal growth and development. When students are primarily compared to their past selves, it encourages a mindset of self-improvement and personal achievement. They can recognize their own progress over time and appreciate their individual journey. This helps combat imposter syndrome, a psychological phenomenon where individuals doubt their abilities and feel like they do not belong or deserve their achievements. By highlighting their improvement, students can gain confidence and overcome feelings of inadequacy. Additionally, while end results and raw scores will always have their place, improvement scores are indispensable in providing a multidimensional understanding of student learning. They offer actionable insights, not just on student abilities but on the effectiveness of the entire educational process.

Furthermore, educators can design a system that consistently provides feedback to motivate students’ improvement. This feedback can be both qualitative and quantitative, offering specific guidance on areas for improvement while also acknowledging the progress made. By receiving regular feedback, students can understand their strengths and weaknesses and work towards enhancing their knowledge and skills. In this context, the educator’s role becomes crucial in creating a supportive and encouraging learning environment via constructive feedback, identifying areas for growth, and offering guidance and resources to help students continuously improve [64]. By adopting this approach, educators can foster a growth mindset among students, where effort and learning are valued over fixed abilities.

In conclusion, our study demonstrated significant differences between the baseline, actual, and new test scores, with the actual and new scores showing higher mean performance compared to the baseline scores. These findings highlighted the potential impact of educational practices on student outcomes and underscored the importance of continued research to identify the most effective approaches for enhancing student achievement.

5. New Ideas about Grading and the Evolution of New Technology

Crucial positive corrective measures are needed to alleviate the negative effects of the modern grading system. One essential and beneficial action has been recommended by Feldman [8], who emphasized instituting an array of beneficial options for students to demonstrate their learning. The range of alternatives can include projects, presentations, portfolios, and other forms of assessment that allow students to show what they know and can do in a variety of contexts. By using these practices, educators can create grading systems that are more inclusive and embrace the diverse strengths and abilities of all students [8,26]. The primary purpose of grading is to assess learning; thus, educators must use numerous methods to achieve this objective. All educators are cognizant of the fact that every student has some prior knowledge that is based on previous exposure, superstition, cultural experience, or personal beliefs. However, many conclude that students’ prior knowledge results in misconceptions, which have been shown to be incorrect [57]. Crogman et al. [57] have shown that students’ basic knowledge can be assessed by the concept inventory (CI) and, furthermore, it successfully differentiates student misunderstandings or misconceptions to build instruction [56]. Evaluating prior knowledge helps educators build better grading systems that focus on student improvement.

Let us return to the data presented in Table 4 and consider the student who scored poorly on the baseline assessment and then scored below the baseline on the first assessment test. Let us further assume there was learning in other class assignments, such as quizzes, discussion engagement, and worksheets. Based on a review of the totality of the student’s performance, this student is operating at a slower pace than the rest of the class. If the instructor attempted to reduce the pace to match this student’s learning needs, it potentially could disrupt the learning process for the class. Thus, we propose a hyperflex learning strategy with more one-on-one peer interaction in a self-paced online venue.

This practice of using a hyperflex learning strategy for students working at a slower pace is an inclusive approach to addressing diverse learning needs. This method combines one-on-one peer interaction and self-paced online learning to help struggling students without disrupting the class’s overall progress. Incorporating AI tools into this strategy can further enhance its effectiveness in supporting learners who need additional assistance. Further, differentiated instruction plays a critical role in meeting students’ diverse learning needs and ensuring their success [37]. Moreover, AI-powered adaptive learning platforms can analyze a student’s performance and learning patterns, recommending appropriate resources and activities to bridge knowledge gaps and consolidate understanding. Peer mentorship programs, facilitated by collaborative AI tools, encourage collaboration and enhance academic performance for students who need additional support [65].

Furthermore, regular formative assessments, supported by AI-generated insights, help in identifying challenges and monitoring progress, leading to personalized learning plans [66]. These strategies foster flexible learning environments, inclusive of various in-person, online, and blended learning options, all of which are responsive to students’ diverse needs [67]. For example, AI-powered tutoring systems can provide instant feedback and guidance to students working at a slower pace, allowing them to address misconceptions and improve their understanding without waiting for teacher intervention. For the successful integration of these instructional practices, ongoing professional development for teachers, potentially augmented by AI-driven training materials, is necessary. Training ensures the effective implementation of strategies that support students with diverse learning needs [68]. By considering these ideas and incorporating AI tools, the hyperflex learning strategy can foster an inclusive and adaptable learning environment for all students, regardless of their learning pace.

In the contemporary educational landscape, the integration of AI-powered adaptive learning tools is emerging as a transformative force. These tools employ machine learning algorithms designed to capture and analyze real-time student data as they interact with the learning materials. This encompasses monitoring their patterns of behavior, accuracy in responses, and even the response times to different types of questions or challenges. Based on these analyses, the AI system dynamically adjusts instructional content, supplementary resources, or even the difficulty level of materials, ensuring it is optimally aligned with each student’s unique learning pace and style.

For illustrative purposes, consider a scenario wherein a student grapples with understanding quadratic equations. If the AI system discerns consistent patterns of errors or prolonged response times, it might proactively provide additional resources to the student. This could take the form of revisiting foundational concepts or providing a set of simpler problems, enabling the student to build confidence and solidify understanding before progressing to more complex challenges.

Beyond adaptive learning, the system’s capability extends to providing incisive, personalized feedback. Unlike traditional generic feedback, the AI tool crafts messages tailored to a student’s specific performance metrics. For instance, post-assignment, a student might be greeted with feedback like, “Your proficiency in solving linear equations is evident. However, there were challenges with quadratic equations—revisiting Chapter 5 might be beneficial.” Such feedback is not only more relevant but also actionable, guiding the student on precise next steps.

From the educator’s vantage point, the system offers dashboards that consolidate and visualize student data, giving insights into individual and collective progress, strengths, and areas needing more focus. This paves the way for teachers to fine-tune their instruction, enabling strategies like grouping students for collaborative tasks based on proficiency levels or even orchestrating one-on-one sessions to address specific learning needs.

For the student, this AI-enhanced environment is not just responsive; it is anticipatory. Students who consistently excel in certain topics might find themselves presented with advanced challenges or further exploration avenues. In contrast, those needing additional support will find the system preemptively offering targeted resources or revision materials, ensuring timely intervention well before formal assessments.

Another strategy for making learning accessible, more experiential, and more equitable is the use of a reward system that keeps students engaged and helps them to succeed every semester. The strategy is a reward system (mock banking system) stimulates curiosity and participative interaction in the classroom without focusing solely on grades. The process starts by assessing students’ basic knowledge and academic skills, then building groups based on their strengths and areas for growth. This builds cohesion and a sense of comfortability in the classroom. Students earn 0.5 points for meaningful interventions, creative ideas, outside resources shared in class, discussion boards, assignments, or labs. These accumulated points can be used as credit “money” for up to two missed attendances or low-grade assignments and exams. This approach promotes creativity and personal accountability without the faculty having to pressure students to participate. It also complements other pedagogical techniques, such as just-in-time learning, humor, gamification, and intentional check-ins with struggling students [30]. The reward system approach has consistently received high evaluations and resulted in student success and transformative learning experiences. In addition, it gives faculty a holistic picture of each student and a set of traits and parameters to use in building the groups students will be a part of for the remainder of the term.

In making decisions about teaching strategies, it is important to be aware that the ones with the capacity to detect flaws or growth areas have the greatest potential to understand what is needed for student success. Baseline tests can be helpful in identifying flaws in students’ understanding or skills, and integrating experiential learning and AI can enhance their effectiveness. These tests provide an initial assessment of students’ knowledge and abilities before instruction begins, allowing educators to tailor their teaching strategies to address any gaps or weaknesses. Three common types of baseline tests include concept inventories, skills tests, and problem tests described below:

• Concept Inventories: These tests assess students’ understanding of key concepts and principles in a specific subject area. By incorporating experiential learning, such as hands-on activities or real-world examples, teachers can better identify and address misconceptions or gaps in understanding. AI tools can also be used to analyze students’ responses and help educators identify common misconceptions more efficiently.
• Skills Tests: Skills tests measure students’ proficiency in specific skills, such as reading, writing, math, or problem-solving. Integrating experiential learning, such as collaborative projects or immersive simulations, can help students develop and refine these skills more effectively. AI can be employed to provide personalized feedback and guidance based on each student’s performance, helping them focus on areas that need improvement.
• Problem Tests: Problem tests present students with real-world or theoretical problems that require critical thinking, analysis, and problem-solving skills. By incorporating experiential learning, such as case studies or role-playing scenarios, teachers can enhance students’ ability to apply their knowledge to complex problems. AI tools can help educators identify patterns in students’ problem-solving approaches, enabling targeted instruction or practice to address specific weaknesses.

By using these types of baseline tests, as well as incorporating experiential learning and AI technologies, educators can more effectively identify and address flaws in students’ understanding, skills, or problem-solving abilities. This comprehensive approach ensures that students receive the support and guidance they need to succeed in their learning journey.

In addition to baseline assessments, one teaching strategy that identifies flaws and offers inventions to solve them is the problem-based learning (PBL) approach, which actively engages students in solving real-world problems. In PBL, teachers present students with a complex, open-ended problem, often related to real-world situations. Then, students work collaboratively in small groups to analyze the problem, identify the underlying flaws or challenges, and research potential solutions. Throughout the process, students develop critical thinking, problem-solving, and communication skills as they propose and defend their solutions. The role of the teacher in PBL is to facilitate the learning process, guiding students by asking probing questions, providing resources, and helping them refine their solutions. By implementing the PBL approach in the classroom, educators can effectively identify flaws in existing systems or processes and empower students to create innovative solutions to address these challenges.

A final option is the recently proposed strategy that focuses on the core of an individual’s learning process by utilizing questioning and asking techniques [58,59]. The Generated Question Learning Model (GQLM), along with its modified version, encourages students to actively engage with the subject matter, identify gaps in their knowledge, and seek solutions to address these gaps. By generating their own questions, students become more invested in the learning process, develop critical thinking skills, and take ownership of their education. This teaching strategy can be an effective tool for educators to identify flaws in students’ understanding and empower them to find ways to address these challenges.

To foster the most effective learning for students with diverse needs, it should be experiential, allowing educators to identify students’ flaws and create pedagogies that not only address these weaknesses but also foster a path for self-directed learning [64]. Experiential learning, grounded in Kolb’s Experiential Learning Theory, emphasizes hands-on experiences, real-world applications, and active engagement, which help students better understand and retain knowledge while developing essential skills [12]. In this approach, educators guide students, provide feedback, and facilitate reflection, using their observations to design targeted interventions for students’ challenges. Furthermore, recent advancements in technology, such as artificial intelligence and adaptive learning systems [69], can enhance experiential learning by providing personalized feedback and resources tailored to individual student needs. Overall, by prioritizing experiential learning, educators empower students to take ownership of their education and develop the skills necessary for lifelong learning.

6. Conclusions

The evolution of grading systems and the infusion of new technologies in the educational sector have revolutionized the way we approach teaching and learning. The adoption of diversified and inclusive assessment methods, such as the concept inventory or baseline assessment instruments, provides a more comprehensive evaluation of students’ knowledge and understanding. These tools delve beyond simple test scores, uncovering misconceptions and gaps in students’ comprehension, thus allowing educators to tailor their teaching to address these issues effectively.

Overall, focusing on students’ improvement scores has the potential to positively impact the learning experience. It removes the negative aspects of competition and comparison, empowers students to recognize their growth, and encourages continuous improvement. With the guidance of educators, this approach can motivate and support students in their educational journey while addressing imposter syndrome and promoting a positive classroom dynamic.

Incorporating experiential learning methods, such as problem-based learning (PBL) and the generated question learning model (GQLM), into the curriculum further enhances this tailored approach. These pedagogical strategies actively engage students in the learning process, fostering critical thinking skills, encouraging personal accountability, and instilling a sense of ownership over their education. Through the application of real-world problems and the generation of their own questions, students become more invested in their learning, leading to a deeper understanding and better retention of knowledge.

Furthermore, the development and implementation of hyperflex learning strategies, augmented by artificial intelligence (AI), and adaptive learning platforms, meet the diverse learning needs of all students. This approach combines one-on-one peer interaction with self-paced online learning, offering a solution for students who may operate at a slower pace than their classmates. AI-powered tools can analyze students’ performance and learning patterns, providing personalized feedback and recommendations, thereby supporting learners who need additional assistance.

In addition to these strategies, creative engagement techniques, such as mock banking reward systems, have been introduced to foster student participation and motivation. By rewarding meaningful interventions, creative ideas, and shared resources, educators can promote active participation and personal accountability without exerting undue pressure on students. This system, in combination with other pedagogical techniques, provides a holistic view of each student’s capabilities and fosters a sense of comfortability and cohesion within the classroom.

Lastly, regular formative assessments, facilitated by AI analytics, offer real-time insights into students’ progress and challenges. These insights, combined with the provision of flexible learning environments, enable the creation of personalized learning plans tailored to individual needs. By integrating AI-powered tutoring systems, instant feedback and guidance can be provided to students, allowing them to address misconceptions and improve their understanding in a timely manner.

By interweaving these innovative strategies with experiential learning, educators are empowered to create a dynamic and adaptable learning environment that caters to the diverse needs of all students. These approaches promote student ownership of education, encouraging them to become active participants in their learning journey. Ultimately, the fusion of these strategies and technologies sets the stage for lifelong learning, equipping students with the skills and knowledge they need to navigate their futures successfully. Fundamentally, they are also powerful antidotes to modern grading systems infused with capitalist ideological tenets and assumptions.