The evaluation method of college teachers’ morality considering intelligent emotion recognition and data mining algorithm
The evaluation method of college teachers’ morality considering intelligent emotion recognition...
Hu, Xingcui
2023-12-31 00:00:00
APPLIED ARTIFICIAL INTELLIGENCE 2023, VOL. 37, NO. 1, e2186413 (881 pages) https://doi.org/10.1080/08839514.2023.2186413 The evaluation method of college teachers’ morality considering intelligent emotion recognition and data mining algorithm Xingcui Hu School of Education Science (College of Teacher), Yangzhou University, Yangzhou, China ABSTRACT ARTICLE HISTORY Received 29 December 2022 The core of teaching consists of four basic values: dignity, Revised 26 February 2023 truthfulness, fairness, responsibility & freedom. All teaching is Accepted 27 February 2023 founded on ethics – whether it be the teacher-student relation- ship, pluralism, or a teacher’s relationship with their work. This article combines intelligent emotion detection technology and a data mining algorithm to develop a model for evaluating college professors’ morals to discover an effective way to do so. This study examines the moral hazard game model of con- cealed behaviors of instructors with overconfidence in four risk preference combinations based on fair fundamental assump- tions and comparisons with rational teachers with the same risk preference. In addition, this research formulates the ideal incen- tive contract for each circumstance. It creates a model of the assessment system of college professors’ morality in conjunc- tion with their real teaching environment. In addition, the simu- lation model is used to assess the influence of the assessment system on the morality of college instructors. The experimental investigation indicates that the assessment technique of college professors’ morality presented in this work, taking intelligent emotion detection and data mining algorithm into account, has some influence. The model and data mining algorithm are applied to evaluate college teachers’ morality, the method effect is statistically evaluated, and the results showing the superiority of the proposed method are obtained. Introduction Only the classroom behavior based on a complete understanding of the moral implication of the classroom is the behavior for itself. Some researchers take the selection of students to raise their hands to speak as an example to study the value education contained in the organizational management behavior of classroom teaching. The research results point out that the selection of stu- dents to raise their hands to speak has evolved from simply stimulating students’ interest and testing teaching effects to carrying out value education and class culture construction in the new era. Only by fully understanding the CONTACT Xingcui Hu huxc@yzu.edu.cn School of Education Science (College of Teacher), Yangzhou University, Yangzhou China © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e2186413-860 X. HU values contained in it and guiding students correctly can educational beha- viors demonstrate value connotations (Dubois, Gruzd, and Jacobson 2020). Moreover, the understanding of the moral connotation of the classroom is the understanding of the moral connotation and meaning of the classroom beha- vior. Specifically, it is the understanding of whether the classroom behavior is in line with the educational goals (Ulbricht 2020). The educational goal is essentially the manifestation of human needs in the field of education, and the understanding of the moral implication of the classroom is to understand the moral value of the classroom. “‘Value’ is a subjective description of the relationship between the subject and the object, which represents the nature and degree of the subjectification process of the object, that is, the existence, attributes and lawful changes of the object are consistent, consistent or consistent with the scale of the subject. The nature and degree of proximity (Comandé and Schneider 2018).” Specifically, on the one hand, we must grasp the law of classroom behavior from the perspective of the object, that is, we must recognize that classroom behavior must be “law- ful;” on the other hand, we must recognize from the perspective of the subject that classroom behavior must be consistent with the educational goals are the same, that is, “fit for purpose.” In classroom teaching, teachers realize that their behavior must be “lawful” and “in accordance with the purpose,” which is the fundamental requirement of “self-determination” in the value judgment of teachers’ classroom teaching (Petersen, Tanner, and Munsie 2019). Artificial Intelligence in Education (AIED) research needs to consider issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion, as well as differentiate between doing ethical things and doing things ethically, understand and make pedagogical choices that are ethical, and account for unintended consequences. A well-designed framework for engaging with the ethics of AIED is needed, combining a multidisciplinary approach and a set of robust guidelines (Holmes et al. 2022). On the other hand, understanding student behavior is vital for virtual system developers and online education designers to integrate virtual technologies properly. This study (Sepasgozar 2022) describes designing and deploying novel virtual tour (VT) modules to serve on-the-job training needs using a case-based narrative scenario as the instructional method. The results indicate that VTAM, in conjunction with contextual learning, immersion, and social presence, has the greatest influence on student engagement, which has a good effect on student satisfaction. The most basic rule of teachers’ classroom teaching is to comply with the laws of students’ physical and mental development. Whether it is the design of teaching objectives and the preparation of teaching content before class, or the use of teaching methods and the adjustment of teaching strategies in the course of classroom implementation, teachers must always check whether their presuppositions and behaviors conform to the laws of students’ physical APPLIED ARTIFICIAL INTELLIGENCE e2186413-861 and mental development. This kind of law exists and is manifested in the students’ life world, so paying attention to the students’ physical and mental development law in the classroom is bound to concern the students’ life world (Williamson and Eynon 2020). “As soon as life and education collide, educa- tion will play an immediate role.” If classroom teaching does not enter into students’ lives and integrates with students’ daily life, it is impossible to truly impress students’ minds and bodies, and it is impossible to fundamentally promote students’ further development explore. Such classroom teaching has caused students’ physical and mental harm to a certain extent, and the class- room has become an inescapable bondage and “torture.” Such a class has also become a “virtuous” class (Taylor and Pagliari 2018). Teachers’ classroom teaching values “fit for purpose,” the most fundamental is to meet the devel- opment of people’s body and mind. This should also be the fundamental value orientation of teachers’ classroom teaching. Only behaviors that meet the requirements of physical and mental development are the greatest goodness and true beauty. As generally recognized by the academic community, beauty is in the form of truth and content with goodness. The truth here is the aforementioned “regularity,” and the goodness is the purpose (Kennedy 2018). Through analysis, it can be found that the value orientation of teachers in classroom teaching will inevitably lead to the unity of truth, goodness and beauty, and its core is goodness. Only kindness can reconcile the balance between what students “want” in educational practice and what teachers (or educational designers) think students “should want.” Kindness has become the fundamental direction for reconciling classroom activities, and teachers’ requirement of “fitness for purpose” in classroom teaching finally turns into concern for kindness. Kindness is not only the common direction of teachers and students’ classroom activities, but also regulates the classroom behavior of teachers and students (Liang et al. 2018). In the whole teaching process of colleges and universities, harmonious teacher-student relationship is the key factor to promote the smooth develop- ment of teaching work, and teachers have a decisive influence on the harmo- nious teacher-student relationship, which is related to the quality of the entire teaching work. Teachers’ noble moral consciousness and noble moral style can promote the formation of a harmonious teacher-student relationship. As a college teacher, they have professional academic level, rigorous work style, noble moral sentiment, take students as the main object of service, and devote themselves to it. Only in the teaching and education work will you be recog- nized and loved by the students. As the main body of teaching and educating people, teachers can only truly achieve “preaching, accepting careers and dispelling doubts” (Etter et al. 2018). Establish a harmonious teacher-student relationship, tolerate students psychologically, communicate with students emotionally, treat students as friends in life, and treat students as interactive objects in teaching, so that students can enjoy what they learn and benefit from e2186413-862 X. HU Promote students’ enthusiasm for learning, stimulate students’ unlimited exploration ability, improve students’ comprehensive quality, and improve teachers’ teaching quality. If teachers do not have noble professional ethics, then teachers will not be able to love education in the process of teaching and educating people. There is a grudge between teachers and students, and students are not happy to learn, which affects the smooth progress of teaching activities, and is not conducive to the improvement of students’ learning level and the improvement of teachers’ teaching efficiency (Trottier 2018). Teachers must constantly strengthen their own ideological understanding and ideological realm in order to improve their professional ethics. They must be keen on scientific research and academic research and development, and constantly improve their academic level, in order to achieve innovation in teaching, rather than blindly citing The work of others (Assumpção et al. 2018). In the process of teaching and educating people, teachers should strengthen the cultivation of professional ethics, and at the same time, they should continue to learn new knowledge and innovate teaching models based on the development of themselves and the teaching system. The guiding role of sexuality and professional ethics in teaching and educating people, so as to achieve the purpose of strengthening ideological understanding and ideologi- cal realm (Chatterjee, Kar, and Mustafa 2021). Ideological understanding and ideological realm belong to ideological and moral understanding. Only by strengthening ideological and moral construc- tion can we establish a correct educational concept. College teachers should adhere to the guiding ideology of educating people to carry out teaching, always put students first, and put students’ teaching work in the most impor- tant position (Berendt, Littlejohn, and Blakemore 2020). In order to strengthen the education of students, teaching is carried out on the basis of students’ mastery of professional knowledge and skills and noble ideology and morality, and the teaching level and quality of teaching shall be continuously improved. Professional ethics, and play the leading role of professional ethics in teaching (Ferguson and Caplan 2021). The teaching content, teaching method and teaching experience are the results obtained through the personal practice and research of countless teachers. It is the verification obtained by teachers through practice on the premise of mastering the theoretical basis, and then imparted to students. In terms of morality, it must also be tested by moral practice, otherwise it is superficial cognition. Practice is the only criterion for testing the truth. Only by applying theory to practice and constantly learning, thinking and changing in the process of practice can we more efficiently and comprehensively under- stand and learn the professional ethics of teachers. Teachers also play the role of managers in school activities. Teachers and students must establish a harmonious teacher-student relationship in order to facilitate teacher man- agement activities. A harmonious teacher-student relationship requires many APPLIED ARTIFICIAL INTELLIGENCE e2186413-863 aspects of communication between teachers and students. Teachers should change their position from the role of teachers to the role of friends. Only with the same level of communication with students can the relationship between the two be closer. When students feel equality and fraternity, students will accept teachers from their hearts. Through communication, teachers can help teachers find some problems and solve them in time, which is conducive to the smooth progress of teachers’ teaching and management (Saltz and Dewar 2019). This paper combines the intelligent emotion recognition technology and data mining algorithm to construct an evaluation model of college teachers’ morality to conduct intelligent evaluation of college teachers’ morality. Intelligent Moral Emotion Recognition The Proposed Method The dual-processing model of moral judgment is developed based on the social intuition model and the traditional rational model. It suggests that moral judgments are jointly driven by three underlying psychological pro- cesses: utilitarian tendencies, deontological tendencies, and individual self- response tendencies. This study used the median segmentation method to classify subjects higher than the median into the high sense of power group. Moral Hazard Game Model for Hidden Actions with Different Risk Appetites and Overconfidence As shown in Figure 1, risk-averse players have a concave utility function with decreasing marginal utility function. A risk-neutral player has a linear utility function with a constant marginal utility function of zero. Risk-loving players have a convex utility function with increasing marginal utility function (Deng, Xu, Gao, et al. 2022). However, the participants with wealth in the economics we consider are basically of diminishing marginal utility, that is, risk averse, and risk neutrality can be regarded as a special case of risk aversion. It is often considered for convenience, but risk-loving participants are often brought up for discussion in gambling problems. Therefore, for formal colleges and universities, this paper does not focus on risk-loving participants, and focuses on risk aversion and risk neutrality (Deng, Xu, Zhao, et al. 2022). Conventional artificial intelligence decision approaches emphasize maximiz- ing expected return (or minimizing the expected cost) (Deng, Zhang, et al. 2022). This is acceptable when decision-makers are risk-agnostic. Nonetheless, many decision-makers are risk-averse and prepared to forego a portion of the anticipated profit to safeguard against catastrophic losses. Early on, the need to minimize risk while making decisions was identified, but it has not been easy e2186413-864 X. HU Figure 1. Utility function of risk appetite. to design risk-capturing models. Effective risk aversion models must be simple to comprehend and interpret for decision-makers; nevertheless, they must also be broad, adaptable, and, most crucially, provide tractable optimization pro- blems (Huang et al. 2023; Xu et al. 2023). Using anticipated utilities is the traditional way of modeling risk aversion, but they are difficult to describe and greatly complicate optimization approaches. This tutorial focuses on the innovative approach to risk aversion based on convex risk assessments. Convex measurements of risk replace the expectation operator with a more generic operator that gives negative outcomes greater weight. In recent years, the development of risk-averse decision-making strategies in artificial intelli- gence and machine learning has garnered increasing attention. Risk aversion is essential to apply machine learning in many practical situations, as risk- neutral solutions to mission-critical issues are sometimes prohibitively dan- gerous. Convex risk metrics and robust optimization are used in classification, multi-armed bandit, and reinforcement learning techniques. Although the overall notion of risk measurements is reasonably straightforward, its actual potency cannot be grasped without a deeper comprehension. For instance, combining risk aversion with sequential decision-making necessitates over- coming various temporal consistency problems. This course will throw light on these challenges and offer several research suggestions. Hypothesis 1: The teacher’s salary income is set as R , which is composed of the teacher’s monthly fixed salary and the corresponding monthly commis- sion, namely θπ. When θ = 0, the management bears all the risks, and the teacher’s risk cost is 0. When θ = 1, the teacher assumes all risks, and the APPLIED ARTIFICIAL INTELLIGENCE e2186413-865 management risk becomes 0. π is an output function, which is related to the teacher’s effort level a (0≤a≤1) and the exogenous uncontrolled variable η η N 2 (such as technical or market uncertainty) that affects the output. ð0; σ Þ, so there is an expression R ðπÞ ¼ wþ θπ; π ¼ aþ η. Hypothesis 2: Teachers will pay the corresponding effort cost C when they work hard, which is related to the degree of effort of the teachers mentioned 1 2 above. We assume that C ðaÞ ¼ ca ; cðc > 0Þ is the cost coefficient of the teachers’ effort. It is not difficult to see that as a increases, C also increases, 0 00 because C ðaÞ> 0; C ðaÞ> 0, C is called a strictly monotonic function. A A Hypothesis 3: The degree of risk aversion of participants is characterized by the degree of risk aversion ρ. The management’s risk aversion degree is expressed as ρ , and the teacher’s risk aversion degree is expressed as ρ , p A that is, when the management and teachers are both risk neutral, ρ = 0, ρ = 0. p A If one of the participants is risk averse, then we assume that the return of 1 2 ρθ σ is exchanged for a certainty-equivalent return. From hypothesis 1 and hypothesis 2, the actual benefit of the teacher = the salary obtained – the cost of effort, then the actual benefit of the teacher is expressed as: r ¼ R C ðaÞ ¼ wþ θðaþ ηÞ