Complex Systems Research in Educational Psychology: Aligning Theory and Method

Formalizing psychological interventions through network control theory

Despite the growing deployment of network representation to comprehend psychological phenomena, the question of whether and how networks can effectively describe the effects of psychological interventions remains elusive. Network control theory, the engineering study of networked interventions, has recently emerged as a viable methodology to characterize and guide interventions. However, there is a scarcity of empirical studies testing the extent to which it can be useful within a psychological context. In this paper, we investigate a representative psychological intervention experiment, use network control theory to model the intervention and predict its effect. Using this data, we showed that: (1) the observed psychological effect, in terms of sensitivity and specificity, relates to the regional network control theoretic metrics (average and modal controllability), (2) the size of change following intervention negatively correlates with a whole-network topology that quantifies the "ease" of change as described by control theory (control energy), and (3) responses after intervention can be predicted based on formal results from control theory. These insights assert that network control theory has significant potential as a tool for investigating psychological interventions. Drawing on this specific example and the overarching framework of network control theory, we further elaborate on the conceptualization of psychological interventions, methodological considerations, and future directions in this burgeoning field.

A conceptual framework and exploratory model for health and social intervention acceptability among African adolescents and youth

Intervention acceptability has become an increasingly key consideration in the development, evaluation and implementation of health and social interventions. However, to date this area of investigation has been constrained by the absence of a consistent definition of acceptability, comprehensive conceptual frameworks disaggregating its components, and few reliable assessment measures. This paper aims to contribute to this gap, by proposing a conceptual framework and exploratory model for acceptability with a specific priority population for health and developmental interventions: adolescents and youth in Africa. We document our multi-staged approach to model development, comprising both inductive and deductive components, and both systematic and interpretative review methods. This included thematic analyses of respective acceptability definitions and findings, from 55 studies assessing acceptability of 60 interventions conducted with young people aged 10-24 in (mainly Southern and Eastern) Africa over a decade; a consideration of these findings in relation to Sekhon et al.'s Theoretical Framework of Acceptability (TFA); a cross-disciplinary review of acceptability definitions and models; a review of key health behavioural change models; and expert consultation with interdisciplinary researchers. Our proposed framework incorporates nine component constructs: affective attitude, intervention understanding, perceived positive effects, relevance, perceived social acceptability, burden, ethicality, perceived negative effects and self-efficacy. We discuss the rationale for the inclusion and definition of each component, highlighting key behavioural models that adopt similar constructs. We then extend this framework to develop an exploratory model for acceptability with young people, that links the framework components to each other and to intervention engagement. Acceptability is represented as an emergent property of a complex, adaptive system of interacting components, which can influence user engagement directly and indirectly, and in turn be influenced by user engagement. We discuss opportunities for applying and further refining or developing these models, and their value as a point of reference for the development of acceptability assessment tools.

Learning Pathways and Students Performance: A Dynamic Complex System

In this study, learning pathways are modelled by networks constructed from the log data of student-LMS interactions. These networks capture the sequence of reviewing the learning materials by the students enrolled in a given course. In previous research, the networks of successful students showed a fractal property; meanwhile, the networks of students who failed showed an exponential pattern. This research aims to provide empirical evidence that students' learning pathways have the properties of emergence and non-additivity from a macro level; meanwhile, equifinality (same end of learning process but different learning pathways) is presented at a micro level. Furthermore, the learning pathways of 422 students enrolled in a blended course are classified according to learning performance. These individual learning pathways are modelled by networks from which the relevant learning activities (nodes) are extracted in a sequence by a fractal-based method. The fractal method reduces the number of nodes to be considered relevant. A deep learning network classifies these sequences of each student into passed or failed. The results show that the accuracy of the prediction of the learning performance was 94%, the area under the receiver operating characteristic curve was 97%, and the Matthews correlation was 88%, showing that deep learning networks can model equifinality in complex systems.

Psychometric Model of College Students Based on Time Series Analysis and Its Application in Educational Management

The psychological measurement method of college students is a hot issue in the field of educational management research. Based on the time series analysis theory, this paper constructs a psychological measurement model for college students. This paper analyzes the psychometric behavior data, uses the time series analysis method for behavior prediction, deeply mines the relevant component information of the psychometric data, and solves the problems of weak correlation between the functions of the psychometric platform and low data accuracy of the psychometric model. At the same time, taking the intervention target group and the intervention mode as the basic variables of the intervention classification system, combining these two dimensions, a two-dimensional classification framework for psychometric intervention was proposed, and four types of different psychometric intervention measures were applied. During the simulation process, a psychometric trajectory matrix was constructed, and a two-dimensional data extraction network was used to extract the psychometric pattern data of a certain period of time. The experimental results show that using the student mental state data as a label can obtain a low-coupling training set classification for psychometric effects of college students.

A Two-Parameter Fractional Tsallis Decision Tree

Decision trees are decision support data mining tools that create, as the name suggests, a tree-like model. The classical C4.5 decision tree, based on the Shannon entropy, is a simple algorithm to calculate the gain ratio and then split the attributes based on this entropy measure. Tsallis and Renyi entropies (instead of Shannon) can be employed to generate a decision tree with better results. In practice, the entropic index parameter of these entropies is tuned to outperform the classical decision trees. However, this process is carried out by testing a range of values for a given database, which is time-consuming and unfeasible for massive data. This paper introduces a decision tree based on a two-parameter fractional Tsallis entropy. We propose a constructionist approach to the representation of databases as complex networks that enable us an efficient computation of the parameters of this entropy using the box-covering algorithm and renormalization of the complex network. The experimental results support the conclusion that the two-parameter fractional Tsallis entropy is a more sensitive measure than parametric Renyi, Tsallis, and Gini index precedents for a decision tree classifier.

Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things

To improve the teaching effect of western music history, the curriculum reform of history education needs to be promoted under the background of the Internet of Things (IoT). At first, a discussion is made on the characteristics of history course, which is combined with the characteristics of teaching data easy to collect under the background of IoT. An analysis is conducted on the related theory of educational data mining. Then, the concept of personalized recommendation is proposed based on deep learning (DL) algorithm. Finally, online and offline experiments are designed to verify the performance of the algorithm from review and investigation, smoothness, and participation of difficulty. The research results show that in terms of offline recommendation accuracy, the average record length in Math data set is 24.5, which is much smaller than that in range data set. The research has obvious innovation significance compared with other studies. In the process of target review and investigation, it is found that the research method here involves a wider range of knowledge and higher reliability. In terms of the difficulty of recommending questions, the Deep Reinforcement Exercise (DRE) recommendation algorithm can adaptively adjust the difficulty of recommending questions. It also allows students to set different learning goals through participation goals. But in the experiments on Math data set, Step 10's recommendation results are not very good, and the difficulty level varies greatly. If the goal setting is high, the problem recommended to students is too difficult, students may answer these questions wrongly, forcing the algorithm to adjust the difficulty adaptively. According to the above results, DRE recommendation algorithm can adapt to different learning needs and customize the recommendation results, thus opening up a new path for the teaching of western music history. Besides, the combination of DL algorithm and western music history teaching design can recommend learning materials, which is of great significance in the teaching of history courses.

Analysis of Literary Situation and Reconstruction of the Writing Subject in Literary Education by Educational Psychology

Educational psychology focuses on the laws of change in the knowledge, skills, and individual psychology of the educatees in the process of education and teaching. Writing teaching is a key and difficult point in literature teaching. Nowadays, it is common for students to be afraid and tired of writing in school literature education. In view of these problems, the present work optimizes the teaching mode of writing from the perspective of reconstructing the writing subject. Through literature research and interdisciplinary analysis, a questionnaire is designed to analyze the literary situation and the reconstruction of writing subjects in literary education. The questionnaire is aimed at three aspects, namely the hidden educational effect of teachers' personality charm, the influencing factors of students' psychology and students' learning effect, and the influencing factors of psychology of the communication between teachers and students and teachers' teaching effect. Then, the changes of students' performance in literary class in these three aspects before and after using the teaching strategy of writing subject reconstruction are analyzed. Finally, the changes of students' grades in the literary course are investigated. In this experiment, a total of 400 questionnaires were distributed, and a total of 389 questionnaires were collected. The survey results show that the number of students who feel the classroom atmosphere is active increases by 10%, the number of students who listen carefully and take notes increases by 7%, and 45% of students have improved their grades. Besides, after the implementation of the teaching strategy, most students change their attitude to the literature course, become more active, and significantly improve their motivation for learning. This study has a certain reference value for the analysis of literary situations and the reconstruction of writing subjects in literary education from the perspective of educational psychology.

Trajectories of Idea Emergence in Dialogic Collaborative Problem Solving: Toward a Complex Dynamic Systems Perspective

According to the complex dynamic systems (CDS) perspective, learning emerges at various system levels. This study built a coherent theoretical framework based on CDS and Bakhtinian dialogic theory and further employed the concept of attractor (i.e., certain stable states that recur over time) in CDS theory to investigate the trajectories of idea emergence and how they diversified group outcomes in dialogic collaborative problem solving (D-CPS). Two contrasting groups were compared using visual and qualitative analysis approaches. The analysis based on idea tree diagrams showed that new ideas emergent in group discussion tended to attract local utterances and performed features of attractors in CDS in both high-performing and low-performing groups. The analysis based on idea hierarchy diagrams revealed how ideas emerged at various system levels. It was also found that status problems were likely to affect the functioning of regulative feedback loops, which might give rise to different structures of idea evolution. This study proposed CDS theory as an alternative perspective, augmented by the ethical considerations of Bakhtinian dialogism, for examining the dynamics of D-CPS.

The Teaching Pattern of Law Majors Using Artificial Intelligence and Deep Neural Network Under Educational Psychology

With the increasing attention to the cultivation of legal talents, a new teaching model has been explored through artificial intelligence (AI) technology under educational psychology, which focuses on improving learning initiative, teaching methods, and teaching quality of students. First, the application of AI and deep neural network (DNN) algorithms are reviewed in education, and the advantages and disadvantages of traditional learning material recommendation algorithms are summarized. Then, a personalized learning material recommendation algorithm is put forward based on DNN, together with an adaptive learning system based on DNN. Finally, the traditional user-based collaborative filtering (UserCF) model and lifelong topic modeling (LTM) algorithm are introduced as the control group to verify the performance of the proposed recommendation system. The results show that the best learning rate of model training is 0.0001, the best dropout value is 0.5, and the best batch size is 32. The proposed personalized learning resource recommendation method based on deep learning (DL) still has good stability under various training data scales. The personalized test questions of recommended students are moderately difficult. It is easier to recommend materials according to the acquisition of knowledge points and the practicability of the recommended test questions of students. Personalized learning material recommendation algorithm based on AI can timely feedback needs of students, thereby improving the effect of classroom teaching. Using the combination of AI and DL algorithms in teaching design, students can complete targeted personalized learning assignments, which is of great significance to cultivate high-level legal professionals.

Educational Psychology-Based Strategy for Instrumental Music Teaching in Normal College

The study is intended to explore the teaching mode of instrumental music teaching in normal college, so as to have qualified instrumental music teaching talents. Based on relevant content of educational psychology, the current instrumental music teaching model of normal college should be reformed in terms of curriculum content, teaching mode, and teaching methods, and a more systematic and standardized education model should be established. As per Orff's music teaching method, a music recommendation model is established based on the convolutional neural network model to provide students with a positive and happy learning environment for instrumental music, and music materials that meet their personal preferences and performance level through user data. The outcomes show that the designed music recommendation model has a music recommendation accuracy rate of 0.3 and a recall rate of 0.29 when the recommendation list is 30, which conforms to the general rules of the music recommendation system. The study is expected to provide reference for establishment of a standardized and systematic instrumental music teaching strategy in normal college.

The Role of Nested Systems in EFL Students' Willingness to Communicate (WTC) and Engagement

Language learning is a complex process with many intrapersonal and interpersonal processes which are nested within smaller systems, themselves. Willingness to communicate (WTC) and engagement of students are two of the many complicated, multifaceted, and dynamic variables in L2 learning that have mostly been explored via quantitative, correlational, and one-shot methodologies. However, such a research trend provided only a snapshot of variables of second language acquisition (SLA) nature and dynamism. Against this shortcoming, this study aims to present the conceptualizations, applications, and implications of complexity dynamic system theory for investigating L2 earner-psychology variables, especially WTC and engagement. In doing so, the definitions, dimensions, and key properties of the two constructs were explained. In the end, a series of research gaps, implications, and future directions are suggested for future researchers in this territory.

Acceptability of digital health interventions: embracing the complexity

Acceptability is a core concept in digital health. Available frameworks have not clearly articulated why and how researchers, practitioners and policy makers may wish to study the concept of acceptability. Here, we aim to discuss (i) the ways in which acceptability might differ from closely related concepts, including user engagement; (ii) the utility of the concept of acceptability in digital health research and practice; (iii) social and cultural norms that influence acceptability; and (iv) pragmatic means of measuring acceptability, within and beyond the research process. Our intention is not to offer solutions to these open questions but to initiate a debate within the digital health community. We conducted a narrative review of theoretical and empirical examples from the literature. First, we argue that acceptability may usefully be considered an emergent property of a complex, adaptive system of interacting components (e.g., affective attitude, beliefs), which in turn influences (and is influenced by) user engagement. Second, acceptability is important due to its ability to predict and explain key outcomes of interest, including user engagement and intervention effectiveness. Third, precisely what people find acceptable is deeply contextualized and interlinked with prevailing social and cultural norms. Understanding and designing for such norms (e.g., through drawing on principles of user centered design) is therefore key. Finally, there is a lack of standard acceptability measures and thresholds. Star ratings coupled with free-text responses may provide a pragmatic means of capturing acceptability. Acceptability is a multifaceted concept, which may usefully be studied with a complexity science lens.

Bifurcations and the Emergence of L2 Syntactic Structures in a Complex Dynamic System

We report on a complex dynamic systems study of an untutored adult French learner’s development of English syntax, specifically two non-finite adverbial constructions. The study was conducted over one academic year of 30 weeks. From an analysis of L2 speech samples collected weekly, certain patterns in the flux emerged. The learner’s ensuing second language development is characterized by a series of bifurcations, stemming from forms competing for the same functional terrain. Each bifurcation is accompanied by turbulence as the system moves from one attractor state to another. The transition is characterized by loss of stability, an increase in variability, and a period of dysfluency. It is in the dynamic relationship of accuracy and fluency that novel syntactic forms emerge, both convergent with and divergent from dominant contextual patterns, with dominance established by consulting a well-known corpus of contemporary English. Non-linear development occurs with continuous and iterative exposure to and interaction in English—from relexification to adaptation and synchronization, animated by the learner’s perception and memory of regular sequential associations, to pruning of divergent forms. What results over time is a branching hierarchy, connecting online processing with over time development. Multiple competing forms continue to co-exist in the learner’s repertoire, which is likely more typical of adult L2 development than of L1 acquisition.

New Directions in Talent Development Research: A Developmental Systems Perspective

Talent development constitutes an important phenomenon of human development; yet it is rarely considered a mainstream topic in the field of child and adolescent development. Conversely, in the field of gifted and talented studies, various talent development (TD) models developed in the past have had minimal interaction with the literature on child and adolescent development, despite the urgent need for TD models and theories that focus on developmental processes rather than individual traits. In this article, I first identify reasons why talent development has been overlooked, and why existing TD models were not able to pick up the slack. I then introduce developmental systems approaches as a new trend in research, and explain how it reinstates the topic of talent development in developmental research, and by so doing enriches our understanding of human development. I then explore the viability of talent development research and theoretical development being guided by the developmental systems framework. Finally, I identify potential contributions of talent development research to the field of child and adolescent development. My overarching argument is that a relational developmental systems approach to talent development provides a new window for the field to explore the nature and development of human potential.