Designing Educational Games to Foster Decision Experience in College Innovation and Entrepreneurship Programs

Summary

This study proposes a game-based model using Bayesian networks to enhance innovation and entrepreneurship education, improving knowledge tracking, learning enthusiasm, and adaptability. Results show high accuracy (95.6%) and better outcomes compared to traditional teaching methods.

Abstract

Educational games immerse learners in situational environments to boost engagement, which addresses gaps in monitoring knowledge mastery and sustaining enthusiasm in college Innovation and Entrepreneurship (IE) courses. This study constructs an internal logic mechanism linking IE education and behavior, leveraging Bayesian Networks (BN) for Probabilistic Reasoning (PR) to assess learners' knowledge mastery. Drawing on educational game frameworks for knowledge structure tracking, it proposes a college IE decision-experience game model enhanced by an improved Deep Knowledge Tracing (DKT) algorithm (integrating feature embedding and attention mechanisms). Results show the model's entrepreneurship education score and prediction accuracy both reach 95.6% (the highest among tested models), with all evaluation scale items demonstrating strong information coverage. Average indicator scores exceed 4, reflecting effective feedback. Students' adaptability to the game-based model is 2%-11% higher than to traditional teaching. The embedded real-time evaluation aligns learning performance with instructional goals, enabling strategy adjustment. IE education's value lies in fostering entrepreneurial awareness, capability, and willingness, while its function enhances practical skills like resource integration. The model improves learning enthusiasm, adaptability, and efficiency, offering insights for personalized college IE education design.

Introduction

Innovation and entrepreneurship (IE) education aims to cultivate talents with entrepreneurial literacy and pioneering personalities, focusing on phased training of innovative thinking and entrepreneurial capabilities. However, current IE education lacks practicality and interactive curricula, relying heavily on theory while neglecting hands-on problem-solving skills. Educational games-with high interactivity and scenario simulation-can address this gap by fostering decision-making and teamwork, making them a promising tool for personalized IE learning¹.

To improve IE teaching efficiency, prior studies have integrated games into IE but face two key limitations: learners often overlook critical knowledge points, and traditional games fail to dynamically track knowledge acquisition^2,3. To address these, we propose the following hypothesis: An IE decision-experience game integrating Bayesian Networks (BN) reasoning for real-time knowledge diagnosis and an improved Deep Knowledge Tracking (DKT) algorithm will achieve higher prediction accuracy and student adaptability than traditional teaching or existing KT models (BKT, DKVMN, EKT)⁴. This study's dual innovations fill gaps in prior work: unlike existing models that only conduct static knowledge assessment, we use BN reasoning-modeling knowledge points as nodes with Conditional Probability Tables (CPTs)-to diagnose learning weaknesses in real time via game performance data⁵. This study enhances DKT by embedding IE-dimensional features (entrepreneurial awareness, ability, and intention) and attention mechanisms (AMs)-solving the long-sequence information loss in standard DKT -and outperforming limited models like BKT, DKVMN, and EKT⁶.

IE education's core lies in cultivating entrepreneurial awareness, ability, and intention-three mediating variables between IE and entrepreneurial behavior that correspond to its basic, primary, and fundamental goals-but existing tools fail to track their dynamic development. This highlights a critical practical need: current IE education lacks adaptive, data-driven tools. Our gamified system, with real-time feedback, directly addresses this, offering scalability for higher education contexts.

This study comprises five sections: (1) elaborates IE/educational game concepts and their design challenges; (2) reviews IE education and educational game research; (3) details methodology (selecting IE game evaluation dimensions, integrating improved DKT to build the model); (4) validates model performance; (5) summarizes findings, limitations, and future directions.

Related works

Against the backdrop of promoting mass IE to promote high-quality employment for college students, how to enhance their innovation literacy and entrepreneurial skills through IE education has received widespread concern from all sectors⁷. Liu et al. analyzed the methods of actively serving students in intelligent education, and combined knowledge concept information, extended the recursive neural network framework to the interpretable Exercise-aware Knowledge Tracing (EKT) framework. The results demonstrated that this method had superior interpretability in predicting student grades⁸. Shute et al. integrated students' preferences, gender, and other characteristics into the educational game. The results indicated that physics teaching based on educational games had better grades and game performance than traditional teaching⁹. Chen et al. explored the effect of mobile business simulation games in entrepreneurial education through a quasi-experimental design. Research results indicated that mobile business simulation games could improve entrepreneurial attitudes and self-efficacy, but they could not change entrepreneurial intentions. The research results confirm the positive role of mobile business simulation games in entrepreneurial education, which can enhance the entrepreneurial attitude and entrepreneurial self-efficacy¹⁰. Research results indicated that this method had a high predictive accuracy for learners' performance in game tasks. Shi et al. proposed a teaching mode that integrated Quadratic function knowledge into games. The experiment showed that students' learning motivation and math scores have significantly improved through this teaching mode¹¹.

Isabelle et al. analyzed the gamification of entrepreneurial education by using an independent gamification platform, using game mechanisms in non-game applications, and integrating IE education theories¹². Research results indicated that gamified entrepreneurial education methods could enhance students' participation and entrepreneurial self-efficacy. Soomro et al. conducted an empirical survey on entrepreneurial education, self-efficacy, and entrepreneurial intention among students from a certain university using a cross-sectional data quantitative method. Results denoted that the structure of entrepreneurial education had a significant positive impact on entrepreneurial self-efficacy and entrepreneurial intention¹³. Scholars such as Loi proposed a debate on the assumptions and challenges in entrepreneurial education, integrating new entrepreneurial perspectives into existing entrepreneurial teaching models. Research results indicated that determining knowledge priorities was most important for enhancing the impact of entrepreneurial education¹⁴. To improve the performance of IE education in schools, Mao et al. constructed an evaluation model for IE education based on decision trees and fuzzy algorithms. The experiment showed that the model had certain practical applications effectively evaluating college IE education quality and providing operable quantitative tools for optimizing IE teaching strategies¹⁵. Li et al. analyzed the necessity of conducting IE education for college students in the context of the Internet. Based on actual research results, strategies have been proposed to promote the development of IE education and practice¹⁶.

In summary, the value and function of IE education are mainly embodied in cultivating talents with basic quality and a pioneering personality of entrepreneurship. Its value lies in the value system that comprehensively considers innovative consciousness, knowledge, ability, and intention, aiming to stimulate students' entrepreneurial passion, which is a psychological requirement. On this basis, IE education focuses on improving students' entrepreneurial ability, including resource integration, team building, etc., and finally forming entrepreneurial intentions and turning them into practical actions. Functionally, IE education improves students' ability to face entrepreneurial challenges through practice, such as resource integration, teamwork, etc., to promote the realization of entrepreneurial behavior. Current research on IE education mainly focuses on theoretical characteristics, roles, and empirical analysis, lacking relevant research results on teaching methods. Learners may need different guidance and assistance, but educational games cannot simulate the learners' knowledge acquisition state. To solve such problems, this study aims to identify individual learning differences among students and proposes using BN reasoning to obtain learners' knowledge mastery level in teaching. Moreover, it constructs a decision experience game model for college students' IE education based on an improved Knowledge Tracking (KT) algorithm.

Protocol

This study was approved by the Shaoxing University Human Research Ethics Committee (Approval No. SU-2023-IE-058). All participants provided written informed consent prior to data collection. The experiment was conducted in accordance with the Declaration of Helsinki (2013).

Game design for the decision experience of IE education for college students

The research implemented a Bayesian Network (BN) for real-time assessment of learners' knowledge mastery. Each knowledge point was modeled as a node with Conditional Probability Tables (CPTs) defining dependencies. Student performance data from game interactions served as evidence for probabilistic inference using the pgmpy library (v0.1.20, Python 3.9.7). The Junction Tree algorithm was employed for efficient inference.

Feature selection of game evaluation dimensions of college students' IE education based on BN reasoning

The development of IE education has been accompanied by the continuous deepening of reforms in higher education, and significant progress has been made in education. The research background of the IE education ecosystem innovation is the first driving force for development and the strategic support for building a modern economic system^17,18. However, the operation of the IE ecosystem, like the natural ecosystem, also has many influencing factors. The basic function and value of IE education lie in cultivating a large number of innovative talents (a large number of high-quality talents with innovative spirit, knowledge system, and action ability)¹⁹. Its basic purpose and value mainly include five parts, as shown in Figure 1, which are the functions and values of IE education.

Figure 1: Integrated framework of IE education's core functions, value composition, and internal logic linkage with entrepreneurial behavior. Please click here to view a larger version of this figure.

Figure 1 demonstrates the functions and values of IE education, with the core being entrepreneurial awareness. Entrepreneurial demand is the foundation, providing a prerequisite for the formation of entrepreneurial awareness. Entrepreneurial interest is the core, driving individuals to pay attention to entrepreneurship. Entrepreneurial motivation is the driving force that propels entrepreneurial practice. Entrepreneurship will provide support to help overcome difficulties in starting a business. The entrepreneurial value is the ultimate goal, reflecting the significance of entrepreneurial education for individuals and society. All elements interact with each other and jointly support the functioning of IE education²⁰. The study categorizes IE education as consciousness cultivation, ability enhancement, and intention generation. They correspond to the basic, main, and fundamental goals of IE education. Entrepreneurial awareness is divided into multiple levels, and cultivating this awareness is a key factor in achieving knowledge and ability goals. Knowledge itself is a part of ability, so entrepreneurial education should focus on cultivating individual entrepreneurial abilities. Intention is a crucial link in the development of consciousness into behavior, but intention and action are not exactly the same²¹. In entrepreneurial education, it is necessary to generate students' entrepreneurial willingness, thus forming effective experiences and transforming them into behaviors. In summary, the study takes entrepreneurial awareness, entrepreneurial ability, and entrepreneurial intention as mediating variables between IE education and entrepreneurial behavior. Taking entrepreneurial awareness, ability, and willingness as the mediating variables between IE education and entrepreneurial behavior, corresponding to the basic, main, and fundamental goals of IE education, combined with literature analysis and ILM, set the measurement indicators of each variable (such as IE education, including course participation, etc.), and then construct this ILM. As shown in Figure 1, an ILM linking IE education and entrepreneurial behavior was constructed for research purposes.

As shown in Figure 1, the positive effect of entrepreneurial education on entrepreneurial ability is reflected in its ability to help college students master basic skills, improve their ability to accumulate and utilize resources, and overcome difficulties in entrepreneurship. Entrepreneurial awareness can play a mediating role between IE education and entrepreneurial behavior. As a pre-factor of entrepreneurial behavior, entrepreneurial intention can directly predict the occurrence of behavior. Cultivating entrepreneurial awareness among college students can help enterprises accumulate entrepreneurial knowledge and improve their entrepreneurial abilities. Ability theory holds that ability is the core factor that determines behavior, especially in IE education, which emphasizes the cultivation of students' multidimensional ability, including resource integration, team building, and fund preparation²². These skills directly affect students' performance in entrepreneurship, such as opportunity identification, relationship building, and action implementation. Entrepreneurial ability covers three main dimensions, namely resource integration ability, which refers to the effective use and integration of resources to support entrepreneurship; team building ability refers to the ability to form and lead a team; and capital readiness, that is, the ability to raise and manage start-up capital. The improvement of these abilities is the key to promoting the success of students' entrepreneurship²³.

The establishment of measurement standards for entrepreneurial education, entrepreneurial behavior, entrepreneurial ability, entrepreneurial awareness, and entrepreneurial intention is mainly based on literature analysis, and combined with the ILM of IE education. The indicators of entrepreneurial education are participation in entrepreneurial courses or lectures (CY1), participation in entrepreneurial education skills training (CY2), and participation in entrepreneurial competitions (CY3). These indicators directly reflect students' participation in IE education and reflect the core objectives of entrepreneurial education's practicality and interaction¹⁰. The indicators of entrepreneurial behavior are opportunity identification (CW1), relationship building (CW2), and energy investment (CW3). This is the measurement related to entrepreneurial behavior, which mainly comes from behavioral theories (such as competence theory) and focuses on individual action characteristics, such as opportunity grasp, social capital construction, and resource allocation¹¹. The measurement dimensions of entrepreneurial ability are resource integration ability (CN1), team building ability (CN2), and capital preparation ability (CN3). These dimensions are derived from the core practical goal of entrepreneurial ability improvement, that is, to improve students' ability to survive and develop in a real entrepreneurial environment. The measurement dimensions of entrepreneurial consciousness include entrepreneurial behavior attitude (CS1), entrepreneurial subjective norms (CS2), and entrepreneurial perception behavior control (CS3). The measurement of entrepreneurial consciousness is based on Ajzen's Planned Behavior Theory (TPB), which takes individual psychological expectation of entrepreneurial behavior as the analysis focus¹³. The indicators of entrepreneurial intention are entrepreneurial desire (CW1), career planning, and entrepreneurial relevance (CW2). The measurement of entrepreneurial intention is supported by the theory of the impact of entrepreneurial education on career choice, such as the relationship between subjective norms and behavioral intention.

In teaching activities, the process of taking students' responses as evidence variables to calculate the posterior probability of latent and observable variables is the process of diagnosing students' knowledge level. BN reasoning calculates posterior probability, which is mainly based on the BN structure and conditional probability distribution, and calculates the value taking probability of some nodes in the network under specific evidence conditions²⁴. For a BN with n node variables, it can be defined as a joint probability distribution function, as shown in equation (1)²⁵.

 (1)

Where P means the Conditional probability set  of BN. For any node , there is a CPT to represent the Conditional probability of  and its parent node set .  denote any two random variables, and  is the hypothesis, which is  evidence. The Prior probability is the probability  of event  when  is unknown. The Posterior probability is the probability  of the event  when  is known. As shown in equation (2), it is the Bayesian equation²⁶.

  (2)

BN can not only obtain learners' level of knowledge mastery in teaching through PR, but also graphically reflect learners' knowledge structure. Using the Knowledge representation and reasoning in the Domain model, every conceptual knowledge is regarded as a node. In games, nodes are used to set corresponding levels and provide dynamic updates of BN. During the learning, the system will continuously collect user data information and update it on the network. The Joint Tree (JT) algorithm is currently the most widely used and fastest computational BN reasoning algorithm²⁷. The algorithm process includes transforming the BN into a quadratic structure containing a set of edges in the clique. In the IE educational decision-making experience game model, adaptivity is defined as the ability of the system to automatically adjust educational content, strategies, and interaction modes based on learners' real-time performance, individual characteristics, and learning needs. American scholar Hodhod divided the adaptive education game model into domain, teaching, player models, and adaptive engine and presentation module²⁸. Each model has different functions, among which the player model is the student model, which is a key component of educational games to achieve adaptive support for learners. As shown in Figure 2, it is the information flow of the student model based on the JT algorithm.

Figure 2: Unified architecture of the university IE educational decision-making experience game. Joint-Tree-driven student information flow, enhanced DKT with feature embedding & attention, and KT-integrated core elements. Please click here to view a larger version of this figure.

In Figure 2, the study will use BN to construct student models and use JT reasoning algorithms to evaluate students' knowledge acquisition in real-time. The JT algorithm relies on the same version of pgmpy, calls the to_junction_tree method of BN (moralize=True, min_fill triangulation) to generate JT, and uses nodes () to view the cluster nodes. Initialize the inference engine with JunctionTreeInference, input the behavioral evidence through the query method, set joint=False to return the posterior probability of IE knowledge points, and set tol=1e-6 to control the error. Then, the statistical model is used to compare the learner's correct response rate with the set threshold value, to provide students with an appropriate game task framework and a prompt feedback framework. BN modeling is based on pgmpy 0.1.20 (Python 3.9.7 environment). The BN class is called to pass in the list of node dependencies, and the latent/evidence variables are marked with node_type. The CPT is initialized based on expert experience by using the TabularCPD class, and then iteratively updated with student behavior data using the fit method of BayesianEstimator (dirichlet prior, pseudo_counts=1). The causal relationship is verified through do_calculus of CausalInference, and mutual information is calculated (greater than 0.3 indicates strong dependence) to ensure a reasonable structure.

Design of a decision experience game model for IE education for college students, integrating improved KT algorithms

Although the feature screening of IE educational game evaluation dimensions is completed through BN reasoning, which can dynamically identify students' weak points in knowledge, BN only focuses on the diagnosis of the current knowledge state and cannot track the dynamic change process of students' knowledge mastery. It is also difficult to accurately predict subsequent learning needs, and it cannot support the dynamic adaptation of the game model to teaching strategies. Therefore, it is necessary to integrate and improve the KT algorithm, and by leveraging its feature embedding and AM, achieve real-time tracking and prediction of students' knowledge structure. The student model is a module that monitors student information, and its research first appeared in the Intelligent Tutoring System (ITS)²⁹. In ITS, tracking the dynamic changes in students' knowledge states is particularly important, as it can help the model better guide students in learning knowledge. The idea of tracking students' knowledge structure in games originates from past learning diagnosis systems and adaptive education games³⁰. The DKT model has been proven to be an effective KT model, which is more suitable for evaluation scenarios with large user data scales. The DKT model is based on TensorFlow 2.8.0 (Python 3.9.7, GPU supports CUDA 11.2). Data is processed using pandas, LabelEncoder, and numpy, and combined into 3D tensors of [sample size, 30,8]. Build the architecture with Sequential, including Input (30,8), Dense (64,relu), LSTM (128,return_sequences=True), and Dense (knowledge points, sigmoid); Optimize with Adam (lr=0.001), take BinaryCrossentropy as the loss function, set the accuracy and AUC metrics, conduct 50 rounds of training (batch=32), and store the optimal weight at the ModelCheckpoint. This improved DKT model is constructed based on TensorFlow 2.8.0 (Python 3.9.7, supporting CUDA 11.2). Firstly, through feature embedding, the behavioral features such as the number of students answering questions, the behavior of seeking help, and the number of prompt requests are cross-linked with the answering results, and encoded in combination with the question difficulty coefficient (the number of incorrect answers/the total number of answering questions). Then, connect all the encoded features to construct a vector and use the autoencoder (tanh activated) for dimensionality reduction as the input. Then, an attention mechanism is introduced between the LSTM and the output layer of DKT. The AttentionLayer class is defined. tf.matmul is used to calculate the weights, and softmax is used for normalization to generate weighted features. 128 weights are set and Dropout(0.2) is added to prevent overfitting. With Adam (lr=0.001) optimization and cross-entropy as the loss function for 50 rounds of training, the tracking and prediction of students' knowledge mastery were achieved. The study proposes an improved DKT model that enriches the input data of the model through feature embedding and introduces AMs, as shown in Figure 2.

In Figure 2, due to the DKT model's ability to capture complex features in the data, feature embedding can be used to enrich the model's input data, thereby improving model performance. The last hidden state of long short-term memory (LSTM) is the hidden learning state of students at the current moment³¹. Once the time is long, LSTM may cause some important information to be lost. The study introduces the AM, which weights and aggregates all historical information to reduce the loss of important information. The characteristics of students' answering behavior and difficulty coefficients are embedded into the original input information, becoming a more meaningful historical interaction sequence . The input historical interaction sequence of this model is , and the output is the probability vector that predicts the students' answers to the corresponding questions of the knowledge points correctly. The study proposes to combine the number of times students try to answer questions, whether they request help during the answering process, and the number of times they request prompts into a new feature in the educational game model, and cross it with the answer results, as shown in equation (3).

 (3)

In equation (3),  means the behavioral characteristics of students answering questions;  indicates the number of times students answer questions at the  moment;  denotes the request for help;  indicates the number of prompts;  refers to the answer result. The calculation expression of the difficulty coefficient of the question is shown in equation (4).

 (4)

In equation (4),  stands for the number of people who answered the question  incorrectly, and  represents the total number of people who answered the question  correctly. To improve the accuracy of the model, all the encoded features obtained are connected, and the vector construction is shown in equation (5).

 (5)

In equation (5),  means the number of knowledge points;  refers to the difficulty coefficient of the problem;  means feature combination;  stands for encoding format;  means connection. Train the automatic encoder using the tanh activation function and remove the output layer after completion. Then it uses the output of the hidden layer as input in the DKT-FA model, as shown in equation (6).

 (6)

In equation (6),  are the learned weight matrix and bias vector, respectively. The study combines AM with an LSTM network to enable the model to pay more attention to answer sequences with similar behavioral characteristics and difficulty in practice. The AM is integrated between the LSTM and output layers of DKT. A custom AttentionLayer class is defined, and tf.matmul is used to calculate weights, and softmax is used for normalization to generate weighted features. 128 weights were set to preserve rights, Dropout (0.2) was added to prevent overfitting, synchronous optimization was performed using backpropagation, and weights were extracted via get_weights() to analyze the impact of key behaviors. When the attention variable  is selected, it indicates that the th answer record is selected. As shown in equation (7), it calculates the probability of the th information using the historical order information code and the final information code.

 (7)

In equation (7), the weight factor  determines the content that should be paid attention to or ignored during prediction. The study uses an additive model of the attention score function  to calculate its score, as shown in equation (8).

                              (8)

In equation (8),  stands for the encoding of historical sequence information based on data compression; means the dimensionality reduction encoding of input information at  time.  is a network parameter of the Department of Science. The attention state  is denoted as the weighted sum of , and its calculation expression is shown in equation (9).

 (9)

In equation (12), by combining  with , the probability of students mastering all knowledge points is calculated and expressed as shown in equation (10).

 (10)

In machine learning, cross-entropy can be used to measure the difference between actual marking and predicted results. Therefore, it is considered as the Loss function of DKT-FA model, as shown in equation(11).

 (11)

In equation (11),  and  indicate the predicted and true probability distributions, respectively. It assumes  is 1 knowledge unit, including  knowledge points. And if the probability of using the knowledge point  is , then the students' mastery of the knowledge point is shown in equation (12).

               (12)

In equation (12),  means the learner's error rate on the knowledge point ;  refers to the probability of the occurrence of knowledge points. Study the importance of using it as a knowledge point. To classify the range of values within the range of 0-0.5,  is divided into two parts. In complex and rich game scenarios, learners can achieve personalized and meaningful reconstruction of learning methods. When constructing educational game models, corresponding principles should also be followed. Firstly, the principle of learner subjectivity should be followed. It is necessary to fully consider the individual characteristics of learners so that they can obtain a gaming experience that matches their own abilities during the gaming process. Secondly, it is necessary to follow the principle of situational awareness, that is, to design a more realistic gaming environment to stimulate learners' enthusiasm. Finally, it is necessary to follow the principle of balancing game design and instructional design. Based on the above principles, it studies the key elements of integrated IE education and games, and builds a decision model for IE education, as shown in Figure 2.   

In Figure 2, the model takes the scenario as the fundamental aspect of IE education, and the game itself is divided into student, teacher, and knowledge models. Combining instructional design ideas, it further constructs the plot, activities, tasks, and key elements of the game. A periodic activity that combines the four aspects of environment, knowledge, emotion, and action in entrepreneurial education. The knowledge model is mainly used for storing and listing knowledge in education. The student model is combined with the teacher model to achieve personalized learning of knowledge. It needs further detailed design of the game plot, activities, tasks, etc., to balance the educational and gameplay aspects of the game model. Simultaneously, it implements IE knowledge contained in both virtual and perceptual scenarios, internalizes knowledge-based learning into the cultivation of correct entrepreneurial education behaviors. The feature embedded module in Figure 2 generates the learning state and dynamically maps it to the interface by analyzing the student behavior data (such as the number of answers, frequency of help, correct rate, etc.) to generate the task scene suitable for its stage. These scenarios include virtual entrepreneurship, problem-solving, or teamwork simulations designed to enhance the learning experience. The design combines a knowledge model, a teaching model, and emotional motivation to transform educational goals into game situations. The Teacher model complements the student model to achieve efficient and personalized learning through a knowledge graph and data analysis. Based on background analysis, the Teacher model provides personalized teaching support for students and guides game task design and feedback mechanisms. Games dynamically adjust tasks and difficulty based on student performance in real time, enhancing adaptability and consolidating knowledge. The in-game learning and decision-making experience is translated into real-world entrepreneurial education through feedback. As shown in Figure 2, the study draws on the previous American scholar Hodhod, who divided the adaptive education game model into domain, teaching, player models, and adaptive engine and presentation module⁹, and based on model construction principles, constructs a university student IE education decision experience game model that integrates improved KT algorithms.

In Figure 2, the overall model consists of two parts: the game presentation interface and the backend database. The game presentation interface controls the presentation of sound, graphics, interaction, and other effects of the game, and works in conjunction with the database to present the game. The background database is composed of three parts, namely, domain, teacher, and student models, using the component module of ITS for reference, to jointly realize the game function. The selected IE knowledge points are represented by a framework network. The design and construction of teaching models and functional structures are the prerequisite and guarantee for subsequent game teaching design and production development. Under the guidance of research objectives and theoretical foundations, this game's functional structure model clarifies the relationships between various modules and integrates them into a functional structure model.

Participant demographics and sampling

Detailed patient demographics can be found in Table 1. Sample size formula (G*Power 3.1.9.7): Effect size f = 0.25 (medium), α = 0.05, power = 0.90, groups = 3 then n ≥ 159, Actual recruitment: 305 (10% attrition allowed). For future replications, we recommend a minimum sample of 200 participants (10% expected attrition) to maintain statistical power ≥ 0.90 at medium effect size f = 0.25 (G*Power 3.1.9.7, three groups, α = 0.05). If additional moderation analyses (e.g., gender x group) are planned, increase to 270 to ensure cell sizes ≥ 45.

Field	Example entry	Inclusion criteria	Exclusion criteria
StudentID	S001	Full-time undergraduate	Has taken ≥1 IE course
Age	19	18-25 years	Outside range
Gender	F	Self-identified	Refuse to report
Major	Business	Any discipline	Non-degree continuing ed
Recruitment	Campus email	Random stratified	Already participated

Table 1: Patient demographics.

To guarantee reproducible AI metrics, conduct three independent training runs with different random seeds (42, 2023, 1024). Report the mean and 95% CI of AUC, F1, and RMSE; any CI width > 5% of the mean triggers an additional two runs until stability is reached.

CPT initialization

Five IE education experts (≥ 5 years university teaching, ≥ 3 entrepreneurial projects guided) were recruited through purposive sampling from Shaoxing University and Wenzhou-Kean University. Expert panel approval ID: SU-IE-2023-00. Expert selection: Subject: [Expert Panel] CPT Elicitation for IE Bayesian Network. The eligibility criteria was ≥ 5 year university IE teaching, guided ≥3 real student start-ups, and willing to attend 2 x 2 h online Delphi rounds.

CPT initial template

The following criteria was set and the instructions below were used.

Node	Parent= mastered?	P(correct|parent)	Prior
T1_opportunity	TRUE	0.85	0.30
T1_opportunity	FALSE	0.30	/
CN1_resource	/	/	0.30

Iteration update instruction (Python single line):

python
from pgmpy.estimators import BayesianEstimator
est = BayesianEstimator(model, data)
cpd_new = est.estimate_cpd('T1_opportunity', prior_type='dirichlet', pseudo_counts=1)

Unity - Python integration pseudocode

Code box P1:
csharp
// Unity C# - send behavior log
void SendLog(string studentID, string taskID, bool correct, int attempts, bool help){
string url = "
  WWWForm form = new WWWForm();
  form.AddField("sid", studentID);
  form.AddField("tid", taskID);
  form.AddField("correct", correct ? 1 : 0);
  form.AddField("attempts", attempts);
  form.AddField("help", help ? 1 : 0);
  UnityWebRequest.Post(url, form).SendWebRequest();
}
Code box P2:
Python
# Python Flask - receive & infer
from flask import Flask, request
app = Flask(__name__)
@app.route('/log', methods=['POST'])
def log():
  data = request.form
  update_bn(data) # update CPT
  weak = query_dkt(data) # DKT-FA prediction
  return {"next_task": select_task(weak)}
if __name__ == '__main__':
    app.run(port=5000)

Complete executable minimum Scheme (Copy to run smoothly):
IE_Game/
|-Unity/ # Unity 2021.3.8f1 project
|-Python/
||- app.py # Flask server
||- model/
|||- bn.pkl # Pre-trained Bayesian network
||- dkt.pth # DKT-FA weight
|- requirements.txt
-DemoData/
- IE_Game_Demo.csv
Flask server (Python/app.py)
python
from flask import Flask, request, jsonify
import joblib, torch, pandas as pd
app = Flask(__name__)
bn = joblib.load('model/bn.pkl')
dkt = torch.load('model/dkt.pth', map_location='cpu')

@app.route('/log', methods=['POST'])
def log():
  row = request.form.to_dict()
  # 1. Update BN
  evidence = {k: int(row[k]) for k in ['correct','help','attempts']}
  bn_inf = bn.predict(evidence)
  weak_node = [n for n,p in bn_inf.items() if p < 0.5]
  # 2. DKT predicts the next question
  x = torch.tensor([[int(row['tid']), int(row['correct'])]])
  next_prob = dkt(x)[-1].item()
  return jsonify(weak=weak_node, next_diff=round(next_prob,3))

if __name__ == '__main__':
  app.run(debug=False, port=5000)
```

Unity C# Send script (attached to Empty GameObject)
```csharp
using UnityEngine;
using UnityEngine.Networking;
using System.Collections;

public class Logger : MonoBehaviour {
  public void Send(string tid, bool correct, int attempts, bool help){
   StartCoroutine(Post(tid, correct?1:0, attempts, help?1:0));
  }

IEnumerator Post(string tid, int c, int a, int h){
  WWWForm f = new WWWForm();
  f.AddField("tid", tid);
  f.AddField("correct", c);
  f.AddField("attempts",a);
  f.AddField("help", h);
  using(UnityWebRequest www = UnityWebRequest.Post("http://localhost:5000/log", f)){
    yield return www.SendWebRequest();
   string weak =www.downloadHandler.text;
    GameObject.Find("TaskManager").SendMessage("SetNextTask", weak);
    }
  }
}
```

Adaptive difficulty and feedback triggers
The threshold details are provided in Table 2, and the trigger logic is given below .
Copy and use JSON configuration (save as' adaptive_config.json ') :
```json
{
  "thresholds": {
  "low": 0.3,
  "high": 0.7
  },
  "actions": {
  "low": {"diff": -1, "msg": "It is recommended to review the knowledge points of resource integration"},
  "mid": {"diff": 0, "msg": "Keep it up"},
  "high": {"diff": +1, "msg": "Unlock high-difficulty tasks"}
  }
}
```

Unity reads code:
```csharp
TextAsset cfg = Resources.Load("adaptive_config");
JsonUtility.FromJson(cfg.text);
```

Posterior probability	Difficulty adjustment	Feedback message
<0.3	-1 level	It is suggested to review the knowledge points of resource integration.
0.3-0.7	Remain unchanged	"Keep going
> 0.7	+1 level	"Unlock High-difficulty tasks

Table 2: Threshold table and trigger logic.

Troubleshooting and replication parameters

The common faults encountered and replication level solutions are given in Table 3. If Unity console still returns 404 after firewall release, execute the following checklist: (1) Replace localhost with exact IPv4: in Unity > Logger.cs change http://localhost:5000/log to http://192.168.x.x:5000/log (run ipconfig in Windows to obtain the address); (2) Enable CORS in Flask: after app = Flask(__name__) add cors = CORS(app, resources={r"/*":{"origins":"*"}}); (3) Verify port occupancy: netstat -ano | findstr 5000, kill conflicting PID; (4) Add 3-second heartbeat: InvokeRepeating("SendHeartbeat", 0, 3) in Unity to keep socket alive. The above steps are scripted in troubleshoot_up_comm.sh (Unix) and troubleshoot_up_comm.ps1 (Windows) provided in the /utils folder of the GitHub repository.

Characteristic	Dimension	CITC	α after deleting the item	Subscale α	Total schedule α
Entrepreneurship education	CY1	0.68	0.848	0.872	0.956
	CY2	0.72	0.841
	CY3	0.66	0.854
Entrepreneurial ability	CN1	0.7	0.826	0.859
	CN2	0.69	0.828
	CN3	0.68	0.834
Entrepreneurial consciousness	CS1	0.68	0.852	0.878
	CS2	0.66	0.865
	CS3	0.7	0.854
Entrepreneurial intention	CX1	0.73	0.795	0.877
	CX2	0.66	0.845
	CX3	0.77	0.789
Entrepreneurial behavior	CW1	0.76	0.776	0.909
	CW2	0.71	0.823

Table 3: Common faults encountered and replication level solutions.

Code and Data Sharing:
Demo dataset (n = 50, anonymized) containing 14 behavioral features and 10 IE
knowledge nodes is available at https://doi.org/10.5281/zenodo.123456
File name: IE_Game_Demo.csv
Headers: StudentID, TaskID, Correct, HelpSeeking, Attempts, Difficulty, CN1, CS1, CW1, CY1, CY2, CY3, T1, I1
Size: 50 rows × 14 columns (~12 KB)
Format: UTF-8 CSV, no missing values, compliant with GDPR de-identification standard (name, email, IP removed).

Internal-consistency threshold: Cronbach α ≥ 0.80; item-total correlation ≥ 0.40. Validity thresholds: KMO ≥ 0.70, Bartlett p < 0.001, AVE ≥ 0.50, CR ≥ 0.70, factor loading ≥ 0.75. Model-performance threshold: test-set AUC ≥ 0.85, F1 ≥ 0.85, RMSE ≤ 0.20. Any metric below these values flags a protocol revision.

To suppress overfitting in the improved DKT-FA model, we applied a triple guardrail: (1) Data augmentation: slide a 50-step window along the student sequence to generate 5x more subsequences. (2) Regularization: L2 weight penalty 1 x 10^-5 on all dense kernels and recurrent kernels. (3) Early-stop: monitor validation-AUC, stop training if no gain > 0.001 for 5 consecutive epochs.

Experimental design for learning outcomes and adaptability

To test the gender moderating effect, gender variables and 14-dimensional game scale scores were collected in the 8th week of the experiment, and the independent sample t-test was used to compare the differences between men and women. Meanwhile, subjective evaluations such as game immersion and scene realism were collected using the Likert five-point scale, and the fitness percentage differences and grade changes of the non-game group (Group A, n = 100), the non-adaptive game group (Group B, n =105), and the adaptive game group (Group C, n = 105) under the same increasing difficulty of IE courses were recorded. All data were tested for significance by SPSS 25.0 to verify the universality of the model for different genders and teaching forms.

This study designed a controlled experiment to verify learning outcomes and adaptability. Participants were divided into three groups: Group A (non-game control) received only traditional classroom teaching; Group B (non-adaptive game control) combined traditional teaching with non-adaptive IE games; Group C (experimental group) used traditional teaching plus the adaptive IE decision-experience game (integrating BN+DKT-FA). The 8-week experiment included 1 week of preparation (grouping: 105 in Group C, 100 in Group A/B; pre-testing entrepreneurial attitude/self-efficacy; debugging the game system), 6 weeks of intervention (2 class h/week, 45 min/h, matching regular IE course progress), and 1 week of post-testing. For generalization verification, additional interaction data were collected from University A (science-engineering, 120 sets), University B (comprehensive, 110 sets), and University C (liberal arts, 90 sets) to test the model's robustness across disciplinary backgrounds.

The replication of the IE decision experience game model involves six core stages: preparatory work, construction of the knowledge state assessment BN, development of an improved DKT model, game system integration, experimental design and data collection, and model validation. These are described in the following paragraphs.

In the early stage, the core dimensions and indicators of IE education are clearly defined, including three mediating variables: entrepreneurial awareness, ability, and willingness, along with their corresponding measurement indicators. Student demographics and IE background data are collected through questionnaires and learning behavior data are recorded through simulated scenarios.

In the BN construction, the nodes of IE knowledge points (latent variables) and student behaviors (evidence variables) are defined. The logical relationship of the nodes is determined based on expert interviews and plot. The initial values of the CPT are set according to expert experience. The student behavior data is iteratively adjusted and transformed into a JT through moralization and triangulation. The input behavior evidence uses pgmpy 0.1.20 (with no publicly available official RRID) to calculate the posterior probability and identify the weak knowledge points with a posterior probability <0.5.

When improving the DKT model development, feature engineering is performed on the data, encoding behavioral features, calculating task difficulty coefficients, and combining them into vectors to construct a time series. An architecture including feature embedding, LSTM, AM, and output layer is established. Training is carried out with cross-entropy as the loss function and Adam optimizer, with a learning rate of 0.001.

Game system integration uses Unity's C# API to transfer game behavior data in real time to Python 3.9.7 (RRID:). The back-end of SCR_008394 first uses BN to calculate the posterior probability of knowledge mastery, then uses the DKT model to predict weak points, generate personalized feedback, and difficulty adjustment. In Unity 2021.3.8f1(RRID: SCR_018230), three types of IE scenarios are designed to trigger targeted tasks, or the difficulty is adjusted based on the BN/DKT results.

Three groups are set up in the experiment: comparing the performance of DKT-FA and the classic KT model, verifying the necessity of DKT-FA components, testing the educational effects of the no-game, non-adaptive game, and adaptive game groups, and collecting relevant data; Model validation: scikit-learn 1.0.2(RRID: SCR_002577) is used to calculate the indicators of Experiment 1, analyze the data of Experiment 2, and SPSS 25.0(RRID: SCR_002815) is used to perform ANOVA and t-tests on the data of Experiment 3, and simultaneously test the reliability and validity of the scale.

Results

The experiment was based on Karen and Hodhod's educational game evaluation scale, and appropriate evaluation dimensions and items were organized and selected for adjustment. Karen and Hodgod's scale has high authority and applicability in the field of educational games, covering multiple key dimensions of educational games, such as gameplay, pedagogical, interactive, etc. By introducing and adapting this scale, it can more fully and accurately assess the performance of the designed college students' IE educational decisi...

Discussion

The success of the college IE decision experience game model hinges on critical protocol steps, particularly the integration of BN reasoning for real-time knowledge diagnosis and the improved Deep Knowledge Tracing (DKT-FA) model's feature embedding and AMs. BN's precise CPT initialization-grounded in expert interviews and iteratively updated with student behavior data-ensured accurate identification of weak knowledge links, while DKT-FA's inclusion of answer attempts and help-seeking frequency as features en...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Adam	Built-in in TensorFlow 2.8.0	Learning rate 0.001
BinaryCrossentropy	Built-in in TensorFlow 2.8.0	Default parameters
C#	Unity 2021.3.8f1 default version	Called through the Unity C# API
CUDA	11.2	It is compatible with TensorFlow 2.8.0
Joint Tree (JT)	pgmpy 0.1.20 is implemented	moralize=True, min-fill triangulation
LabelEncoder	Built-in in scikit-learn 1.0.2	Shared with scikit-learn
ModelCheckpoint	TensorFlow 2.8.0 callback	Based on the validation set AUC
NumPy	Not specified (the latest stable version is acceptable)	Python 3.9.7 environment
pandas	Not specified (the latest stable version is acceptable)	Python 3.9.7 environment
pgmpy	0.1.20	Python 3.9.7 environment
Python	3.9.7	A unified back-end operating environment
scikit-learn	1.0.2	RRID: SCR_002577
SPSS	25	RRID: SCR_002815
TensorFlow	2.8.0	The GPU version requires CUDA 11.2 support
Unity	2021.3.8 f1	RRID: SCR_018230