Actual Grade Research Articles

The Relationship of Grade Expectation and Academic Achievement of College Students

Students’ motivation is an important factor in academic performance. The purpose of this study is to examine the relationship and interaction effects among college students' grade expectations, academic performance and perceptions of course difficulty. The sample of the study consists of 2,946 students enrolled at an university in Ankara. 11,294 responses of the students to the Teaching Quality Assesment Questionnaire and grades of students form the study’s data. The dependency relationship between students' expected grade and received grades was examined with the Chi-square test of independence. In addition, multilevel frequency analysis and log linear analysis were applied to determine the interactions between the levels of expected grade, received grade and perceived difficulty level of the course variables. The study found that students who received high grades had expectations that closely aligned with their actual grades, while students who received low grades had expectations that were less aligned. In other words, as students' academic achievement increases, their expectations of their grades and the grades they receive align more closely. Another finding is a negative relationship between an increase in the perceived difficulty of the course and the likelihood of expecting and receiving high grades. The findings are discussed in terms of Expectancy Theory, Goal Setting Theory and the Dunning-Kruger effect.

OSCE RELATED ANXIETY IN ASSOCIATION WITH PERCEIVED VS ACTUAL PERFORMANCE

Objectives: The study investigates the impact of anxiety on medical students performance in Objective Structured Clinical Examinations (OSCEs), focusing on the relationship between self-perceived and actual performance. Methods: Conducted among 5th-year medical students at Taif University, the study used an online questionnaire with the PHQ-4 scale to measure anxiety and depression. The aim was to correlate these levels with students self-assessment and actual performance in OSCEs. Results: The study analyzed responses from 62 students. The average self-reported anxiety level was 8.95 ± 1.348 on a 10-point scale, with 64.5% of students reporting severe anxiety and depression. No significant gender differences in anxiety and depression severity were noted (p=0.308). Higher levels of anxiety and depression were reported by students who perceived their grades as lower, although this was not statistically significant (p=0.431). There was no statistically significant correlation between anxiety/depression severity and actual grades (p=0.280). Conclusions: The study underscores the prevalent issue of anxiety among medical students and its potential influence on their self-perceived performance in critical evaluations like OSCEs. It suggests the necessity for targeted interventions to manage student anxiety, which could enhance self-assessment accuracy and overall performance.

面向华裔学生的“听说+读写”中文教学模式探析——以印尼共同希望语言学院2023级大专班为例

<div><p>Teaching model is one of the key factors to improve teaching quality. In view of the tendency of the current international Chinese teaching model to focus on structure and knowledge, but not on pragmatics and ability, the Chinese Language and Literature College of Jinan University in China has innovated a set of "listening and speaking + reading and writing" Chinese teaching model. This model focuses on the mutual coordination of "listening and speaking" and "reading and writing", aiming to quickly improve students' Chinese communication ability. This model has been first practiced overseas at the Common Hope Language School in Indonesia. This article mainly introduces the model and the teaching steps of the listening and speaking class in this model, and uses the students' actual grades to demonstrate the feasibility of this teaching model. The study shows that after about 6 months of intensive study in this model, students' Chinese listening and speaking ability has made significant progress. This model helps to improve the listening and speaking ability of Chinese beginners in a short period of time.<strong></strong></p></div>

Estimation of the Radiographic Parameters for Hallux Valgus From Photography of the Feet Using a Deep Convolutional Neural Network.

Hallux valgus (HV), also known as bunion deformity, is one of the most common forefoot deformities. Early diagnosis and proper evaluation of HV are important because timely management can improve symptoms and quality of life. Here, we propose a deep learning estimation for the radiographic measurement of HV based on a regression network where the input to the algorithm is digital photographs of the forefoot, and the radiographic measurement of HV is computed as output directly. The purpose of our study was to estimate the radiographic parameters of HV using deep learning, to classify the severity by grade, and to assess the agreement of the predicted measurement with the actual radiographic measurement. There were 131 patients enrolled in this study. A total of 248 radiographs and 337 photographs of the feet were acquired. Radiographic parameters, including the HV angle (HVA), M1-M2 angle, and M1-M5 angle, were measured. We constructed a convolutional neural network using Xception and made the classification model into the regression model. Then, we fine-tuned the model using images of the feet and the radiographic parameters. The coefficient of determination (R2) and root mean squared error (RMSE), as well as Cohen's kappa coefficient, were calculated to evaluate the performance of the model. The radiographic parameters, including the HVA, M1-M2 angle, and M1-M5 angle, were predicted with a coefficient of determination (R2)=0.684, root mean squared error (RMSE)=7.91; R2=0.573, RMSE=3.29; R2=0.381, RMSE=5.80, respectively. The present study demonstrated that our model could predict the radiographic parameters of HV from photography. Moreover, the agreement between the expected and actual grade of HV was substantial. This study shows a potential application of a convolutional neural network for the screening of HV.

RESEARCH OF THE STRESS-STRAIN STATE OF PRECAST REINFORCED CONCRETE HOLLOW CORE SLABS UNDER THE ACTION OF A CONCENTRATED AND UNIFORMLY DISTRIBUTED LOAD

The scientific work is devoted to a comprehensive study of the stress-strain state of prefabricated hollow-core floor slabs, establishing the nature of deformation, as well as determining the characteristics of crack resistance, deformability and load-bearing capacity experimentally under short-term loads. The experimental data obtained were confirmed by theoretical calculations. The scientific novelty of the experiments carried out within the framework of the presented work lies in the improvement of the principles of direct design of floor slabs from commercially produced beam slabs, the possibility of determining the stress-strain behavior of a slab structure using the formed ends. The method of monitoring the stressed behavior of a slab structure was also further developed. For the first time, the results of full-scale experimental studies of the stress-strain behavior of a prefabricated slab, carried out using the hydraulic loading method, have been obtained, and the features of the deformation of a slab embedded in the wall of a multi-story building have been experimentally established. The article provides recommendations for the practical use of the research results obtained, and also develops technical specifications for the production of concrete slabs during design and construction. The developed proposals were used in the construction of residential buildings in Kharkiv. When using the recommended grade of concrete C25/30 (instead of the actual grade C20/25 tested), the rigidity of the slab increases and its deflections will be smaller. Based on the data obtained from the experimental testing of the slabs, a generally positive test result should be stated. During further serial production of slabs, periodic testing of batches of slabs should be carried out in accordance with DSTU B V.2.6-53:2008. In this case, the slabs are accepted according to the indicators of strength, rigidity, crack resistance, fire resistance limits, frost resistance limits, as well as water resistance of slabs intended for use in aggressive environments. Keywords: concentrated load, testing, concrete, calculation, deflection, transverse force.

Knowing that you know that you know? An extreme-confidence heuristic can lead to above-chance discrimination of metacognitive performance.

In daily life, we can not only estimate confidence in our inferences ('I'm sure I failed that exam'), but can also estimate whether those feelings of confidence are good predictors of decision accuracy ('I feel sure I failed, but my feeling is probably wrong; I probably passed'). In the lab, by using simple perceptual tasks and collecting trial-by-trial confidence ratings visual metacognition research has repeatedly shown that participants can successfully predict the accuracy of their perceptual choices. Can participants also successfully evaluate 'confidence in confidence' in these tasks? This is the question addressed in this study. Participants performed a simple, two-interval forced choice numerosity task framed as an exam. Confidence judgements were collected in the form of a 'predicted exam grade'. Finally, we collected 'meta-metacognitive' reports in a two-interval forced-choice design: trials were presented in pairs, and participants had to select that in which they thought their confidence (predicted grade) best matched their accuracy (actual grade), effectively minimizing their quadratic scoring rule (QSR) score. Participants successfully selected trials on which their metacognition was better when metacognitive performance was quantified using area under the type 2 ROC (AUROC2) but not when using the 'gold-standard' measure m-ratio. However, further analyses suggested that participants selected trials on which AUROC2 is lower in part via an extreme-confidence heuristic, rather than through explicit evaluation of metacognitive inferences: when restricting analyses to trials on which participants gave the same confidence rating AUROC2 no longer differed as a function of selection, and likewise when we excluded trials on which extreme confidence ratings were given. Together, our results show that participants are able to make effective metacognitive discriminations on their visual confidence ratings, but that explicit 'meta-metacognitive' processes may not be required.

Machine learning model for deep exploration: Utilizing short wavelength infrared (SWIR) of hydrothermal alteration minerals in the Qianchen gold deposit, Jiaodong Peninsula, Eastern China

The Jiaodong Peninsula in the eastern North China Craton is renowned globally as a major gold province. Notably, the Jiaojia orefield is an important gold-producing district in this region, and its depths are inferred to offer substantial prospecting potential, underscoring the need for innovative exploration tools to uncover concealed orebodies. The Qianchen gold deposit in the southern part of the Jiaojia orefield is characterized by the disseminated-stockwork-style orebodies at depths of ca. –1000 to –1800 m, with three mineralization stages, namely (I) pre-ore K-feldspar-quartz stage, (II) quartz-sericite-gold-polymetallic sulfide stage (main Au mineralization stage), and (III) post-ore quartz-calcite stage. Systematic short-wavelength infrared (SWIR) spectral analysis reveals that sericite group minerals developing in Stage II are the predominant and wide-distributed alteration minerals at Qianchen. Their spectral characteristics demonstrate a shift in the Al-OH absorption feature wavelength positions (Pos2200) to longer wavelengths (>2205 nm) within the orebodies. This shift is likely due to strong water–rock interactions leading to Fe and Mg substituting for Al in octahedral sites. Orthogonal partial least squares discriminant analysis (OPLS-DA) indicates that the H2O, OH, and FeOH spectral bands (Variable Importance in the Projection (VIP) ≥ 1) of sericite are crucial for distinguishing between gold ores and barren wall rocks. Additionally, Long Short-Term Memory (LSTM) models demonstrate that spectral data of sericite can be used to predict ore samples with an accuracy of 82 %, validated by receiver operating characteristic analysis (84 %), and blind test results demonstrate that the predicted grade closely aligns with the actual grade. Therefore, it is concluded that SWIR spectral characteristics of sericite group minerals can serve as an effective indicator for prospecting the Qianchen concealed gold mineralization. This study highlights the potential of coupling between machine learning and SWIR spectral data of alteration minerals to uncover the concealed lode gold mineralization.

Grade prediction in the middle east: a post-pandemic case study of the optimism bias.

Evidence exists that the pandemic has brought about stress, and altered study habits and academic performance. No evidence exists regarding whether metacognition has also been altered. The present field study examined the accuracy and confidence with which college students make grade predictions in a general education course after the pandemic. It tested whether one of three types of biases affected students' predictions as a way to cope with the uncertainty of a final exam's outcome: illusion-of-knowing, optimism, and pessimistic bracing. Students made predictions both before and after completing the final exam (summative assessment) to determine the impact of each of the hypothesized biases on estimates made in a context of varying uncertainty. Accuracy was computed as the difference between expected and actual grades on the final exam. Confidence in the predictions made was measured on a Likert scale. Exam performance was categorized as good, poor, or inadequate. In this study, less-than-desirable performance was accompanied by overestimations. However, overestimations were made with little confidence and benefited from the information acquired from completing the exam. This pattern of results suggests that students who are not doing well are not under the spell of the illusion-of-knowing phenomenon. Indeed, their optimistic predictions are punctured by the awareness of a likely undesirable outcome (as indicated by their weak confidence in the predictions made). Implications and applications of these findings are discussed.

Research on the Effectiveness of Tactical Missile Weapon Systems Based on Energy Big Data

Abstract The study of tactical missile weapon systems, particularly their operational efficiency, is increasingly important in modern warfare. These systems are crucial for accurately striking targets and achieving air superiority. The effectiveness evaluation of such missile systems provides a quantitative foundation for both upgrading existing weapons and setting performance benchmarks in combat exercises. This particular topic focuses on the evaluation of tactical missile weapon systems using energy big data. A comprehensive index system is utilized to conduct this evaluation, incorporating methods like fuzzy comprehensive evaluation and analytic hierarchy process. These methods enable both qualitative and quantitative analysis of the capability matrix in the ADC (Aim, Deploy, and Conclude) model. The main objective is to ensure the correctness and effectiveness of the evaluation model. An example analysis is conducted to verify the model’s validity. The results from this analysis indicate a high degree of accuracy in the model’s predictions. The disparity between the actual and expected output results is minimal, with a relative error within 4%. Furthermore, the combat effectiveness grade determined by the model aligns with the actual grade observed, confirming the model’s capability to effectively evaluate the combat effectiveness of surface-to-air missile weapon systems. This research is significant as it provides a reliable and accurate method for assessing the effectiveness of tactical missile systems. Such evaluations are vital for informed decision-making in military strategy and weapon system development. Using energy big data in this context represents a novel approach, reflecting the increasing integration of advanced data analytics in military technology assessments.

A study on predicting students’ grades for ideological and political courses with decision tree generation rules

Predicting students’ course grades is an essential element in teaching. This paper used decision tree generation rules to study the prediction of students’ ideological and political course grades. Firstly, ID3 and C4.5 algorithms were briefly introduced; then, an improved C4.5 algorithm with higher computational efficiency was put forward. The formula of the C4.5 algorithm was optimized using theories such as the Taylor series. Finally, experiments were performed on the UCI dataset and students’ ideological and political course datasets. The results suggested that the average classification accuracy and computation time of the improved C4.5 algorithm was 79.37% and 74.1 ms, respectively, on the UCI dataset, which was better than the traditional C4.5 algorithm. Then, the experiment predicting students’ course grades demonstrated that the average quiz grade and the number of video views had the greatest impact on the final grades. The prediction accuracy of the improved C4.5 algorithm reached 93.46%, and the average computation time was 54.8 ms, which was 19.17% less than the C4.5 algorithm. The experimental results verify the effectiveness of the generation rule of the improved C4.5 algorithm in predicting students’ ideological and political course grades. This algorithm can be applied in the actual grade prediction.

When stereotypes disadvantage boys: Strength of stereotypes in mathematics and language arts and their relations with grades

AbstractThere is growing concern about boys' lagging performance in school, not only in language arts, where the gap is particularly pronounced, but also in mathematics. Stereotypes associating one gender with language arts or with mathematics are likely to contribute to these gaps. Such stereotypes can translate into explicit beliefs such as the extent to which students are aware of societal stereotypes or the extent to which they personally believe stereotypes to be true, but also indirectly into performance following a stereotype threat manipulation. However, few studies have considered these multiple stereotype expressions in both mathematics and language arts to examine their importance in predicting boys' and girls' actual grades in school. To fill this gap, two complementary studies examined high school boys' and girls' awareness and endorsement of stereotypes about both language arts (n = 299) and mathematics (n = 243), as well as whether stereotype threat impaired boys' performance on a spelling test. Although the effect of stereotype threat was not significant overall, our results showed that students were aware of and endorsed strong stereotypes advantaging girls in language arts. In mathematics, students endorsed counter‐traditional stereotypes slightly advantaging girls. Our results also showed that these multiple expressions of stereotypes related to students' grades. In doing so, our work provides insights regarding possible targets for interventions to reduce gender gaps disadvantaging boys in school.

Clinical-radiomics nomogram using contrast-enhanced CT to predict histological grade and survival in pancreatic ductal adenocarcinoma.

Tumor grading is important for prognosis of pancreatic ductal adenocarcinoma (PDAC). In this study, we developed preoperative clinical-radiomics nomograms using features from contrast-enhanced CT (CECT), to discriminate high-grade and low-grade PDAC and predict overall survival (OS). In this single-center, retrospective study conducted from February 2014 to April 2021, consecutive PDAC patients who underwent CECT and had pathologically identified grading were randomized to training (n=200) and test (n=84) cohorts for development of model to predict histological grade based on radiomics scores from CECT (HGrad). Another 42 patients were used as external validation cohort of HGrad. A nomogram (HGnom) was constructed using radiomics score, CA12-5 and smoking to predict histological grade. A second nomogram (Pnom) was constructed using radiomics score, CA12-5, TNM, adjuvant treatment, resection margin and microvascular invasion to predict OS in radical resection patients (217 of 284). Among 326 patients, 122 were high-grade (120 poorly differentiated and 2 undifferentiated). The HGrad yielded AUCs of 0.75 (95% CI: 0.64, 0.85) and 0.76 (95% CI: 0.60, 0.91) in test and validation cohorts. The HGnom achieved AUCs of 0.77 (95% CI: 0.66, 0.87), and the predicted grades calibrated well with actual grades (P=.13). OS was different between the grades predicted by radiomics scores (P=.01). The integrated AUC of the Pnom for predicting OS was 0.80 (95% CI: 0.75, 0.88). Compared with the HGrad using features from CECT, the HGnom demonstrated higher performance for predicting histological grade. The Pnom helped identify patients with high survival outcome in pancreatic ductal adenocarcinoma.

A Fuzzy Approach to Educational Grading Systems “Fuzzy Logic Based Grade Card”

This study aims at developing a fuzzy approach for the educational grading systems. Through this study a fuzzy logic-based grading card is suggested. The grading system based on crisp approach just deals with numbers. Fuzziness, being an important property of language, motivates us to work and study in this fuzzy environment. Before discussing the actual grade card some introduction about the key concepts is given for the readers. We have also analyzed the difference in grading and evaluating systems followed from the decades and the new fuzzy logic-based evaluation system. It also includes connectivity levels, advantages, and disadvantages between both evolution methods. The future scope of the fuzzy grading system is also discussed. The report will conclude with the answer to the question, “Is a fuzzy logic- based grade card worth for the educational grading systems?”. Moreover, towards the end, suggestions will be provided on how to bring more of these fuzzy approaches into education systems.

The extremely overconfident and extremely dissatisfied: a case study of an introductory macroeconomics college course

ABSTRACT In this paper, I first provide additional evidence of the prevalence and severity of the problem of student grade overconfidence. I do so by documenting the level of overconfidence that exists in an introductory macroeconomics course. In this course, 75% of the 614 students earned a grade that was below what they expected. I also find that students who expect that their actual grade will fall significantly below their original expected grade are dramatically more likely to report that they had the worst overall experience in this course relative to their experiences in all other concurrent courses.

The development of the integration of red gene and curriculum thinking based on BP neural network mathematical algorithm

Abstract In the background of the era of digitalization of big data information, the traditional teaching of ideological and political courses faces huge challenges. The innovation of ideological and political courses education is imperative, and the topic of ideological and political education combined with big data technology has attracted the attention and research of ideological educators. This paper focuses on the development of the integration of red genes and curriculum thinking and politics based on the BP neural network mathematical algorithm. Firstly, we briefly understand the concept and principle of the BP neural network mathematical algorithm and algorithm by finding literature, constructing BP neural network model, analyzing the specific strategy of the BP neural network teaching algorithm for the integration of red gene and curriculum Civic and Political development, and data analysis take the ideological and political achievement of a university between 2006 and 2015 as the learning sample and establishes the BP neural network system by conducting the actual achievement and predicted grades data analysis. Then the established model was used to predict the ideological and political grades of the university from 2016 to 2020. The comparison study and discussion were conducted with the actual grades. The results showed that the actual grades of students and the predicted grades both showed a positive linear correlation. The study had a promotional effect on the improvement of students’ grades. The maximum error between the actual and predicted grades was 3, which maximally ensured that the BP neural network-based model predicted the accuracy and stability of the ideological and political grades. This study provides a substantial reference for ideological and political curriculum research and helps students establish correct socialist core values, thus having great historical significance for developing ideological and political education in China

Exploring College Academic Performance of K to12 IT and Non-IT-related Strands to Reduce Academic Deficiencies

Improving students' academic performance is a significant concern among academics. Despite various strategies to improve academic performance, there are still a significant number of students who fail academically. This study sought to investigate the possible reasons for the academic deficiencies among Infotech Development Systems Colleges, Inc. Information Technology (IT) students in the IT and Non-IT-related strands, if their strand is significant to their academic performance in college, as well as formulate a solution based on the identified reasons and recommendations to reduce the negative academic remarks. The researchers employed survey questionnaires and interviews to conduct exploratory data analysis. Similarly, the Senior High School academic performance and actual grades of the respondents from AY 2018-2019 to First Semester of AY 2021-2022. On the tested hypothesis showed statistical significance (p < 0.05) on IT-related strand. The study further reveals that the Non-IT-related strand has more students with academic deficiencies compared to IT-related strand and highlights a variety of reasons cited. Respondents cited misalignment of strand to current program, instructor not speaking clearly, unreliable internet connection, and failure to complete and submit an academic task as reasons for academic deficiencies. The researchers designed a model that can potentially eliminate academic inadequacies. The model takes into account both internal and external factors; for internal variable it includes effective time management, a positive attitude and mindset, prompt and punctual completion of requirements, and good study habits. While for the external factors, competent and student-friendly instructors, a stable, strong, and accessible internet connection, a conducive learning environment, relevant available resources and facilities, adaptation of limited face-to-face or hybrid classes, and alignment of SHS strand to college program of choice are recommended.

In search of the corporate governance risk premium embedded into the cost of capital

The paper proposes to intend the firm as a “nexus of stakeholders”, each bearing return-to-risk expectations about the sharing of the corporate performance. All the stakeholders must achieve their own satisfaction through the bargaining of contracts that must be sustainable, i.e., keep both the firm and its stakeholders-network alive in the long term. Governance is intended as the mechanism that gives a solution to the above puzzle. When both market and contracts are complete, an optimal solution can be easily found. But when incompleteness emerges, governance solutions can misallocate the firm performance among the stakeholders. This is the case when incomplete governance emerges. In fact, in incomplete contests, the stakeholders will negotiate the visible-only arguments of their contracts, this way binding also the invisible ones, i.e., those impacting anyway on their ex-post performance. This being the case, a governance risk premium (GRP) emerges in the medium-long run, impacting equity investors’ return-to-risk performance, thus incentivizing a governance repackage. Such a GRP depends both on the actual grade of market completeness and the one of contracts as per the risk allocation made through time. The proposed methodology to detect GRP is then applied to the Italian case to test its strength. Results show that GRP inflates 39bp the cost of equity capital with the following break-down: 123bp as basic-GRP from operations which is increased +98bp for the GRP-informative component and reduced -191bp by GRP-managerial component; a GRP-behavioural component +90bp would lead GRP from operations up-to 120bp while sharing 81bp with debt capital leads the final figure down to 39bp (i.e., 123 + 98 – 191 + 90 – 81)

When Expectation Isn’t Reality: Racial Disparities in Overestimation and STEM Attrition Among First-Year Students in College

ABSTRACT Existing research indicates that underrepresented-in-STEM racially minoritized students with similar academic preparation are less likely than their counterparts to persist in STEM, raising the question of factors that may contribute to racial disparities in STEM participation beyond academic preparation. We extend the current literature by first examining race-based differences in what students expect to receive and their actual grades in introductory STEM college courses, a phenomenon termed as overestimation. Then, we assess whether overestimation differentially influences STEM interest and persistence in college. Findings indicate that first-year STEM students tend to overestimate their performance in general, and the extent of overestimation is more pronounced among racially minoritized students. Subsequent analyses indicate that students who overestimate are more likely to switch out of STEM, net of academic preparation. Results from regression models suggest that race-based differences in overestimation can be explained by pre-college academic and contextual factors, most notably the high school a student attended.

중학생 과학시험의 문항 유형에 따른 더닝-크루거 효과

Objectives In this study, the purpose of this study is to examine whether the Dunning-Kruger effect, which is a biased confidence in one's ability according to the type of question in the science subject, appears in middle school students, suggesting a learning method, and removing the negative factors of learning. The purpose of this study is to respond to the demands of the school to present a learning strategy that minimizes the discrepancy between academic expectations and academic achievement, and to prepare a basis for implementing it. Methods For 215 second-year coeducational middle school students located in Gyeongsangbuk-do, we had them predict the expected scores for each of the multiple-choice and short-answer types of test questions before the first science test in the second semester of 2021. A significant relationship was verified by analyzing the relationship between quartiles based on actual grades and expected grades, and t-testing. Results A significant negative correlation was confirmed between the expected score and the difference score (the gap between the expected score and the actual score), and it was also confirmed that the overestimation of the short-answer type was larger than that of the multiple-choice type question. By confirming the two-way phenomenon of overestimation of students with low academic achievement and underestimation of students with good grades, the Dunning-Kruger effect, a previous study, was also found in Korean middle school students. Conclusions The correlation between the student's actual achievement score and the difference score was negative (-) in both multiple-choice and short-answer types for each test item type. There is a significant difference between the actual grades and quartiles in estimating the expected scores for multiple-choice and short-answer types. We present a learning strategy to provide opportunities for self-directed learning that accurately judges one's own abilities, expand the question-and-answer-type question type method in the school field, and check whether there are factors that affect cognitive bias in addition to the question type.

A cross-sectional study to validate an administrative back pain severity classification tool based on the graded chronic pain scale

Treatment of chronic lower back pain (CLBP) should be stratified for best medical and economic outcome. To improve the targeting of potential participants for exclusive therapy offers from payers, Freytag et al. developed a tool to classify back pain chronicity classes (CC) based on claim data. The aim of this study was to evaluate the criterion validity of the model. Administrative claim data and self-reported patient information from 3,506 participants (2014–2021) in a private health insurance health management programme in Germany were used to validate the tool. Sensitivity, specificity, and Matthews’ correlation coefficient (MCC) were calculated comparing the prediction with actual grades based on von Korff’s graded chronic pain scale (GCPS). The secondary outcome was an updated view on direct health care costs (€) of patients with back pain (BP) grouped by GCPS. Results showed a fair correlation between predicted CC and actual GCPS grades. A total of 69.7% of all cases were correctly classified. Sensitivity and specificity rates of 54.6 and 76.4% underlined precision. Correlation between CC and GCPS with an MCC of 0.304 also indicated a fair relationship between prediction and observation. Cost data could be clearly grouped by GCPS: the higher the grade, the higher the costs and the use of health care. This was the first study to compare the predicted severity of BP using claim data with the actual severity of BP by GCPS. Based on the results, the usage of CC as a single tool to determine who receives CLBP treatment cannot be recommended. CC is a good tool to segment candidates for specific types of intervention in BP. However, it cannot replace a medical screening at the beginning of an intervention, as the rate of false negatives is too high.Trial registration The study was conducted using routinely collected data from an intervention, which was previously evaluated and registered retrospectively in the German Registry of Clinical Trials under DRKS00015463 (04/09/2018). Informed consent and the self-reported questionnaire have remained unchanged since the study and, therefore, are still valid according to the ethics proposal.