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This paper utilizes the methods of effectiveness prediction and teaching decision-making to construct a precise teaching management framework based on artificial intelligence, and gives a specific plan for carrying out precise teaching interventions in educational practice. This paper investigates the cognition and demand of students in Z province for precise education in college civics and politics through a questionnaire survey. It also researches and verifies precision education through practical research to present its effectiveness. The results show that in terms of the needs and preferences of various aspects of ideological and political education, the majority of students accounted for between 40 and 60%, which is a relatively even distribution, reflecting the diversity of needs. The p-values of the entrance examination and the final examination of the second semester, the final examination of the first semester and the final examination of the second semester are 0.046, 0.004 and 0.18 respectively.Precision teaching can significantly improve students’ Civic and Political Learning Effect.

PurposeCritical thinking is considered important in psychological science because it enables students to make effective decisions and optimizes their performance. Aiming at the challenges and issues of understanding the student's critical thinking, the objective of this study is to analyze online discussion data through an advanced multi-feature fusion modeling (MFFM) approach for automatically and accurately understanding the student's critical thinking levels.Design/methodology/approachAn advanced MFFM approach is proposed in this study. Specifically, with considering the time-series characteristic and the high correlations between adjacent words in discussion contents, the long short-term memory–convolutional neural network (LSTM-CNN) architecture is proposed to extract deep semantic features, and then these semantic features are combined with linguistic and psychological knowledge generated by the LIWC2015 tool as the inputs of full-connected layers to automatically and accurately predict students' critical thinking levels that are hidden in online discussion data.FindingsA series of experiments with 94 students' 7,691 posts were conducted to verify the effectiveness of the proposed approach. The experimental results show that the proposed MFFM approach that combines two types of textual features outperforms baseline methods, and the semantic-based padding can further improve the prediction performance of MFFM. It can achieve 0.8205 overall accuracy and 0.6172 F1 score for the “high” category on the validation dataset. Furthermore, it is found that the semantic features extracted by LSTM-CNN are more powerful for identifying self-introduction or off-topic discussions, while the linguistic, as well as psychological features, can better distinguish the discussion posts with the highest critical thinking level.Originality/valueWith the support of the proposed MFFM approach, online teachers can conveniently and effectively understand the interaction quality of online discussions, which can support instructional decision-making to better promote the student's knowledge construction process and improve learning performance.

Research consistently has found teachers’ use of assessment data for instructional purposes challenging and inconsistent. To support teachers’ use of data, we need to develop shared knowledge about how data are and can be used to advance teaching and learning. However, the literature on the specific actions teachers take is inconsistent, creating challenges for both research and practice. As part of a larger project examining data use in instructional decision making, we developed a framework to classify teachers’ instructional responses to data. Then, we used quantitative and qualitative data from educators across 5 districts and 20 schools to evaluate the utility of the framework. This article documents the process and outcomes of our multistage, mixed-methods approach to these tasks. We conclude with the potential uses of this framework for research and practice.

Developing Rural School Leaders combines a focus on rural education and school leadership development to illustrate how the teaching and learning conditions in rural schools can be enhanced through transformative leadership coaching. By unpacking literature related to rural school leadership development and using case studies to authentically illustrate the complexities involved in rural school leadership development, this book explores how leaders can develop their abilities to increase data-informed instructional decision making, create a culture that supports teaching and learning, and develop other leaders. Ultimately, this important book concludes with an exploration of the opportunities and challenges of developing rural school leaders.

The purpose of this study was to investigate the relationship between instructional design and classroom discourse as implemented by a mathematics teacher. The instructional design in this study distinguished three different types of discussions—framing, conceptual, and application—based on their sequential position, their purpose, the instructional task, and the associated assessment methods. Data included transcripts from nine videotaped class sessions from a US seventh-grade mathematics classroom during a unit on surface area and volume. The findings showed differences in the types of discourse interactions among the three types of discussions. By understanding the relationship between the instructional design and the discourse interactions associated with each discussion type, mathematics teachers may find opportunities to plan classroom discussions with higher quality interactions. In addition, teacher educators and mentor teachers can use the findings from this study to provide explicit assistance and feedback to prospective and newly inducted teachers who want to incorporate classroom discussion into their instructional units.

Early reading development is a complex process that includes the acquisition of skills such as alphabet recognition, phonemic awareness, and vocabulary development. Early educators make important instructional decisions in their classrooms about how to support these skills. Understanding these decisions, and the beliefs and experiences that contribute to them, is critical to informing improvement. This study explored the daily instructional decisions three Head Start teachers made about reading instruction. Specifically, we wanted to examine the relationships between Head Start teachers' professional, practical and personal experiences and their subsequent instructional choices about reading. Using a multiple case study design, classroom observations, questionnaire, and interview data suggested that teachers held strong beliefs about what constituted appropriate reading instruction. While professional knowledge played a limited role in informing these decisions, their abundant practical and personal knowledge was influential. Understanding teachers' beliefs about early reading can be an important first step in bringing about change in instructional practices.

In this study, we explore the instructional decision making of Charlotte, a graduate of an intensive social studies teacher education program. Charlotte articulated a sophisticated conception of historical thinking and appeared to possess exemplary pedagogical content knowledge. Her classroom practice did not incorporate the approaches to historical thinking and inquiry that were discussed in her methods course. She possessed a clear view of her purpose of history teaching, which was to impart a particular set of moral values; her practices were consistent with her purpose; and she controlled her class to accomplish that purpose. /// Dans cet article, les auteures analysent une décision pédagogique de Charlotte, diplômée d'un programme de formation à l'enseignement spécialisé en sciences humaines. Charlotte, qui a développé une conception avant-gardiste de la pensée historique, semble posséder une connaissance exemplaire du sujet. Ses pratiques pédagogiques n'incluent pas les approches discutées dans son cours de méthodologie quant à la pensée et à la recherche historiques. Elle a une notion claire du but qu'elle poursuit en enseignant l'histoire, à savoir la transmission d'un ensemble précis de valeurs morales. Ses pratiques vont de pair avec ce but et elle contrôle sa classe de manière à atteindre son objectif.

This chapter highlights the use of Curriculum-Based Measurement (CBM) data throughout school-based organizational structures to support teachers and administrators in instructional decision making and the determination of effective intervention practices. In recent years, emphasis has been placed on a three-tier model and/or a school-wide model within a Response to Intervention (RTI). As a result, schools analyze data at the classroom, grade, building, and district levels to evaluate core, supplemental, and intensive instructional supports and services. The chapter discusses how General Outcome Measures (GOMs) have been used to support the implementation of a multi-tiered service delivery model within an RTI framework. It outlines how GOMs are used at each of the three service-delivery tiers—tier 1: quality instruction for all students; tier 2: supplemental instruction for some students; and tier 3: intensive instruction.

The growing integration of artificial intelligence (AI) dialogue systems within educational and research settings highlights the importance of learning aids. Despite examination of the ethical concerns associated with these technologies, there is a noticeable gap in investigations on how these ethical issues of AI contribute to students’ over-reliance on AI dialogue systems, and how such over-reliance affects students’ cognitive abilities. Overreliance on AI occurs when users accept AI-generated recommendations without question, leading to errors in task performance in the context of decision-making. This typically arises when individuals struggle to assess the reliability of AI or how much trust to place in its suggestions. This systematic review investigates how students’ over-reliance on AI dialogue systems, particularly those embedded with generative models for academic research and learning, affects their critical cognitive capabilities including decision-making, critical thinking, and analytical reasoning. By using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our systematic review evaluated a body of literature addressing the contributing factors and effects of such over-reliance within educational and research contexts. The comprehensive literature review spanned 14 articles retrieved from four distinguished databases: ProQuest, IEEE Xplore, ScienceDirect, and Web of Science. Our findings indicate that over-reliance stemming from ethical issues of AI impacts cognitive abilities, as individuals increasingly favor fast and optimal solutions over slow ones constrained by practicality. This tendency explains why users prefer efficient cognitive shortcuts, or heuristics, even amidst the ethical issues presented by AI technologies.

The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness. Digital traces of student behavior promise more scalable and finer-grained understanding and support of learning processes, which were previously too costly to obtain with traditional data sources and methodologies. This synthetic review describes the affordances and applications of microlevel (e.g., clickstream data), mesolevel (e.g., text data), and macrolevel (e.g., institutional data) big data. For instance, clickstream data are often used to operationalize and understand knowledge, cognitive strategies, and behavioral processes in order to personalize and enhance instruction and learning. Corpora of student writing are often analyzed with natural language processing techniques to relate linguistic features to cognitive, social, behavioral, and affective processes. Institutional data are often used to improve student and administrational decision making through course guidance systems and early-warning systems. Furthermore, this chapter outlines current challenges of accessing, analyzing, and using big data. Such challenges include balancing data privacy and protection with data sharing and research, training researchers in educational data science methodologies, and navigating the tensions between explanation and prediction. We argue that addressing these challenges is worthwhile given the potential benefits of mining big data in education.