=Paper=
{{Paper
|id=Vol-3667/DC-LAK24-paper-1
|storemode=property
|title=Trustworthy and Explainable AI for Learning Analytics
|pdfUrl=https://ceur-ws.org/Vol-3667/DC-LAK24-paper-1.pdf
|volume=Vol-3667
|authors=Min-Jia Li,Shun-Ting Li,Albert C. M. Yang,Anna Y.Q. Huang,Stephen J.H. Yang
|dblpUrl=https://dblp.org/rec/conf/lak/LiLYHY24
}}
==Trustworthy and Explainable AI for Learning Analytics==
https://ceur-ws.org/Vol-3667/DC-LAK24-paper-1.pdf
Trustworthy and Explainable AI for Learning Analytics
Min-Jia Li 1, Shun-Ting Li1, Albert C. M. Yang2, Anna Y.Q. Huang1 and Stephen J.H. Yang 1*
1 Department of Computer Science and Information Engineering, National Central University, Taiwan
2 Department of Computer Science and Engineering, National Chung Hsing University, Taiwan
*Corresponding author
Abstract
In recent years, there has been a surge of interest in combining artificial intelligence (AI) with education
to enhance learning experiences. However, one major concern is the lack of transparency in AI models,
which hinders our ability to understand their decision-making processes and establish trust in their
outcomes. This study aims to address these challenges by focusing on the implications of explainable
and trustworthy AI in education. The primary objective of this research is to improve trust and
acceptance of AI systems in education by providing comprehensive explanations for model predictions.
By doing so, it seeks to equip stakeholders with a better understanding of the decision-making process
and increase their confidence in the outcomes. Additionally, the study highlights the importance of
evaluation metrics in assessing the quality and effectiveness of explanations generated by explanation
AI models. These metrics serve as vital tools for ensuring reliable system performance and upholding
the fundamental principles necessary for building trustworthy AI.
To accomplish these goals, the study utilizes the LBLS-467 dataset to predict high-risk students,
employing both logistic regression and neural networks as AI models. Subsequently, explanation
artificial intelligence techniques such as LIME (Local Interpretable Model-agnostic Explanations) and
SHAP (Shapley Additive Explanations) are utilized to evaluate students' learning outcomes and provide
explanations. Finally, six evaluation indicators are adopted to assess the accuracy and stability of these
explanations. In conclusion, this study addresses the challenges associated with inconsistencies in
explainable AI models within the field of education. It emphasizes the need for explainability and trust
when applying AI systems in educational contexts. By providing comprehensive explanations and
evaluation metrics, this research empowers education teams to make informed decisions and fosters a
positive environment for the integration of AI. Ultimately, it contributes to the reliable implementation
of AI technologies, enabling their full potential to be harnessed in educational settings for the benefit of
learners and educators alike.
Keywords
Explainable AI, Trustworthy, Learning Analytics 1
1. Introduction
In recent years, artificial intelligence (AI) has been widely used in various fields. AI has shown
great potential in these areas due to its ability to address specific needs within specific domains.
However, as artificial intelligence continues to integrate into our lives, people are increasingly
applying it to the decision-making process. Whether using AI for human resource decision-
making[1]or for triaging and assisting in investigations of AI-related crimes[2], these examples
demonstrate the significant impact of AI on humanity. Against this backdrop, the credibility of
artificial intelligence has become one of the most critical issues of our time.
Although machine learning models were able to identify high-risk students early on, the black-
box nature of these models created challenges in explaining their decision-making process and
predicting outcomes. As a result, education teams have found it difficult to trust the decisions
made by the models, leading to unexpected limitations in the use of AI in education [3]. Therefore,
in recent years, researchers have increasingly combined explainable AI with predicting student
learning outcomes to enable the explainability of model prediction processes[4, 5].
As we strive to build trustworthy artificial intelligence, it is critical to follow certain
fundamental principles to ensure that it functions positively and reliably in a variety of contexts.
LAK-WS 2024: Joint Proceedings of LAK 2024 Workshops, March 18–19, Kyoto, Japan
minjia9916@gmail.com (M. Li); ariel055132@g.ncu.edu.tw (S. Li); albertyang1994@gmail.com (C. M. Yang);
anna.yuqing@gmail.com (Y.Q. Huang); stephen.yang.ac@gmail.com (J. H. Yang)
0000-0003-2168-2816 (C. M. Yang);0000-0002-2075-2256 (Y.Q. Huang); 0000-0003-1059-620X (J. H. Yang)
© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Evaluation is an important aspect to ensure reliability, and evaluation models and indicators need
to be established to evaluate system performance [6, 7].
Advancements in LMS and learning analytics research lead to modular systems storing
personal data in multiple locations. Anonymity is crucial for safeguarding data in integrated
systems.[8]
By combining these aspects, we attempt to address the problem of inconsistent explanations
produced by different explainable AI models when presented with the same dataset. This study
will explore the selection of evaluation metrics to provide a comprehensive approach to this
problem. Ultimately, this work will help strengthen the education team's understanding and trust
in the model and promote the sustainable development of artificial intelligence in the field of
education. The research questions are as follows:
RQ1: What specific evaluation metrics can be employed to assess the quality and effectiveness
of explanations generated by explainable AI models?
RQ2: How to solve the problem of inconsistent explanations produced by different explainable
AI models when presented with the same dataset?
2. Experiment Design
Figure 1 shows the experimental design flowchart outlining the sequential steps involved in
conducting the study. The LBLS-467 dataset was obtained, the data will undergo preprocessing
to handle missing values and identify high-risk and low-risk students. Following that, feature
selection will be conducted to categorize questionnaire questions and learning behaviors into
relevant features, and the data will be normalized. These features will be used for model training.
After training, the model's effectiveness will be evaluated, and SHAP and LIME will be utilized for
model explanation. Finally, six evaluation indicators will assess the quality of explanation.
Figure 1: Experimental Design Flowchart
2.1. Dataset
The LBLS-467 (Learning Behavior Learning Strategy-467) dataset will be utilized as the data
source for this experiment. LBLS-467 is an upgraded version of the LBLS-160 dataset[9]. The
dataset consists of two components: Learning Behavior and Learning Strategy. Under the
Learning Behavior section, the dataset captures students' learning behaviors in two online
learning environments, namely BookRoll and VisCode. BookRoll is a digital material platform
designed to facilitate online learning. Students can access materials, add bookmarks, and
highlight important points. The system automatically records these learning behaviors for further
analysis[10]. VisCode is an online Python development environment. When students practice
using VisCode, the system automatically records their activities, including the time spent, any
errors encountered, and the overall duration, for learning analysis purposes[11].
Regarding Learning Strategy, it collects data on students' self-regulated learning (SRL)
through the use of the Motivated Strategies for Learning Questionnaire (MSLQ) and the Strategy
Inventory of Language Learning (SILL) questionnaire. The MSLQ measures six dimensions of
learning motivation, while the SILL assesses language learning strategies based on Oxford's
categorization of language learning strategies proposed in 1990. It comprises six dimensions with
a total of 50 items[12], although the dataset only includes 48 items as two questions were deemed
irrelevant for programming language learning and were excluded. The modified questionnaire
items were tailored to suit language learning in the context of programming languages[13].
2.2. Data Preprocessing and Feature Extraction
The LBLS-467 dataset was obtained, followed by preprocessing of the data, where missing
values were filled with 0 and questionnaire data with a standard deviation of 0 was removed.
Next, the data was divided into two categories: pass and fail. The fail category represents the 25%
of students with lower learning status, indicating relatively backward academic performance, i.e.,
high-risk students[14].
Table 1 representing learning behaviors, focused on the analysis of student interactions with
the BookRoll platform. We assessed the frequency of e-book openings, page turns, and page skips
as indicators of active learning. Additionally, in the VisCode platform, we collected data to assess
students' programming behaviors. This included recording the total usage time, frequency of
opening VisCode, instances of code copying and pasting, lines of code written, and the overall
count of error codes executed. These indicators provided valuable insights into students'
programming engagement and proficiency.
Table 1
Features of Learning Behavior Extracted from the Learning Environment
System Feature Description
BookRoll Marker_Operation Number of markers added and deleted
Memo_Operation Number of times to add, delete, and modify note
Bookmark_Operation Number of times to add, delete, and skip to a bookmark
Prev_Next_Operation Number of times to turn page
Jump_Operation Number of times to jump to a bookmark, notes or key point
Open_Num Number of open the e-book
Marker_Num Number of markers (highlight key point)
Memo_Num Number of memos
Bookmark_Num Number of bookmarks
VisCode Error_Num Number of errors that occurred when VisCode ran the code
Used_time Total usage time
Code_Length Total number of lines of all programs
Execute_Times Total number of code executions
Notebook_Open Total number of times to open VisCode
Code_Copy Total number of code copies
Code_Paste Total number of times code was pasted
Table 2 presented a comprehensive questionnaire that evaluated various dimensions of
learning strategies. By categorizing the questionnaire items according to the authors'
descriptions, we gained a deep understanding of how students approach their learning. One
example is the SRL (Self-Regulated Learning) questionnaire, which focused on rehearsal
strategies involving repetitive review for better retention and comprehension.
The table encompassed different features and descriptions, including SRL Learning Motivation
(intrinsic and extrinsic motivation, task value, control beliefs, self-efficacy, and test anxiety), SRL
Learning Strategy (rehearsal, elaboration, organization, critical thinking, metacognitive self-
regulation, time and study environment management, effort regulation, peer learning, and help-
seeking), and SILL (Strategy Inventory for Language Learning) strategies (memory, cognitive,
compensation, metacognitive, affective, and social strategies).
Data normalization is an effective data preprocessing strategy for data mining and machine
learning[15-17]. In this study, Min-Max Normalization is employed to scale the feature data to a
range of 0 to 1 while preserving the original data distribution.
Table 2
Learning Strategies Features Extracted from Learning Questionnaires
Questionnaire Feature Description
SRL Learning intrinsic The internal drive and enjoyment individuals
Motivation experience when engaging in learning activities.
extrinsic External factors, such as rewards or recognition,
that influence individuals' engagement in learning
activities.
task_value Assess students' perceptions of the interest,
importance and usefulness of course content
control_beliefs Assesses whether students believe their hard work
will lead to positive outcomes
self_efficacy Assesses the judgment and confidence that the
student can complete the task independently
test_anxiety Student anxiety levels about tests in the course
SRL Learning rehearsal Repetitive or repeated review of study material to
Strategy enhance retention and understanding.
elaboration Enhancing understanding by making connections
and creating meaningful associations with prior
knowledge.
organization The act of structuring and arranging information in
a systematic and logical manner to facilitate
comprehension and retrieval.
critical_thinking The process of objectively analyzing and evaluating
information to make informed judgments and
decisions.
metacognitive_self_regulation The skill of monitoring and controlling one's own
learning process for better outcomes.
time_and_study_environment Encompass managing study time effectively and
creating an optimal setting for focused learning.
effort_regulation The skill of consciously managing and adjusting
one's level of effort to maximize learning
outcomes.
peer_learning The process of students learning from and with
their peers, through collaborative activities and
discussions.
help_seeking Students seek support from others when they
encounter difficulties in their studies.
SILL memory The ability and strategies used to effectively
remember and recall the materials learned.
cognitive The mental processes and abilities involved in
learning, such as attention, memory, thinking, and
problem-solving.
compensation The use of alternative strategies or resources to
overcome difficulties or limitations in language
skills or knowledge.
metacognitive Students will plan, organize, evaluate and monitor
their own language learning
affective Students regulate their emotions, motivations and
attitudes when learning a language
social Student interacts with others while learning
2.3. Model Training, Evaluation and Explanation
Commonly used machine learning models in educational scenarios include random forest (RF),
support vector machine (SVM), decision tree (DT), logistic regression (LR), K-nearest neighbor
algorithm (KNN), and artificial neural network (ANN). Logistic regression and neural networks,
however, have been found to provide more accurate predictions compared to other methods[18,
19]. Hence, this experiment utilizes logistic regression and neural networks for prediction.
To assess the predictive performance of various machine learning algorithms, this study
employs five indicators: Accuracy, Precision, Recall, F1-Measure, and Area Under Curve (AUC).
These indicators are widely used to evaluate the classification performance of models[20, 21]
This study utilizes LIME[22] and SHAP[23] as explanation generators for model predictions.
Six evaluation indicators are employed to assess the quality of explanations, measuring
authenticity and stability[24]: Feature Agreement (FA), Rank Agreement (RA), Prediction Gap on
Important Features (PGI), Prediction Gap on Unimportant Features (PGU), Relative Input
Stability (RIS) and Relative Output Stability (ROS).
The first four evaluation indicators (FA, RA, PGI, PGU) assess the accuracy of the explanations,
while RIS and ROS evaluate their stability. FA and RA are specific to linear models (e.g., linear
regression, logistic regression)[24], whereas PGI, PGU, RIS, and ROS are applicable to all models.
Feature Agreement (FA) quantifies the proportion of the top K features that exhibit consistent
rankings between explanations generated by AI and predictions made by the model. Prediction
Gap on Important Features (PGI) measures the difference in prediction probabilities when
influential features, as identified by explainable AI-generated explanations, are perturbed. Higher
PGI values indicate a stronger correspondence between the explanation and the prediction.
Conversely, Prediction Gap on Unimportant Features (PGU) measures the change in prediction
probabilities when non-influential features, as identified by explainable AI explanations, are
perturbed. PGU serves as an indicator of the explanation's accuracy in capturing non-influential
factors.
Relative Input Stability (RIS) and Relative Output Stability (ROS) quantify the maximum
change in the explanation generated by explainable AI in relation to the predicted input and
output probabilities, respectively. These metrics evaluate the stability of the explanation.
3. Result
3.1. Evaluation of Model Efficacy
Table 3 displays the training performance results using LR and ANN, indicating that logistic
regression outperforms artificial neural networks in predicting whether a student is high-risk or
low-risk.
Table 3
Predictive Performance Results of Different Models
Method Accuracy Precision Recall F1-Score AUC
LR 85.8% 78.9% 85.8% 81.0% 60.8%
ANN 63.9% 65.3% 63.9% 61.3% 58.8%
3.2. Explanation of Discrepancy Results
Both SHAP and LIME can provide explanations for model predictions on individual student
data. SHAP offers explanations through waterfall plots, while LIME utilizes its own graphical
representation.
The waterfall plot in SHAP is designed for analyzing the most important features contributing
to a high-risk prediction for a single data point. The X-axis represents the SHAP value, indicating
the impact (positive or negative) of the corresponding feature on the prediction. The Y-axis
represents the data features and their values for that particular data point (e.g., 337 = Error_Num,
which represents the total number of errors the student encountered while compiling code). The
function f(x) represents the prediction result given by SHAP, considering all features. If f(x) equals
1, it indicates a high-risk student, while f(x) equals 0 represents a low-risk student. E[f(x)]
represents the average prediction value of the model across the dataset.
Figure 2 illustrates how SHAP explains the predictions of a logistic regression model through
a waterfall plot for a high-risk student. The following information can be observed: The majority
of students are predicted as low-risk, as the value of E[f(x)] is 0.111, approaching 0. In the plot,
this student is predicted as high-risk, with a value of 1 for f(x).
The main reason for labeling this student as high-risk is the high value of Error_Num (with a
SHAP value of 0.69), which far exceeds the SHAP values of Marker_Num and Marker_Operation
(0.07 and 0.06, respectively). This indicates that the student's excessive errors during code
compilation are the primary factor contributing to their high-risk prediction.
Throughout the semester, this student made a total of 337 errors while compiling code, with
the third quartile of Error_Num being 152. This confirms the student's tendency to make a
relatively high number of errors during code compilation.
Figure 2 Waterfall plot: Using SHAP to Explain High-Risk Students
Figure 3 demonstrates how LIME explains the predictions of a logistic regression model for
the same student as a high-risk student. From the Prediction Probabilities on the left, it can be
seen that LIME predicts the probability of this student being a low-risk student as 0.25, while the
probability of being a high-risk student is 0.75. This indicates that LIME leans towards
considering this student as a high-risk student.
The middle chart indicates the five most important features and their contributions to the
prediction, as well as the prediction rules. For example, the top five features are Error_Num (total
number of errors during code compilation), Execute_Times (total number of program executions),
Marker_Operation (frequency of using key functions), Prev_Next_Operation (total number of page
flips), and Used_Time (total duration of using VisCode). It is also stated that if Error_Num exceeds
171, LIME considers this student as a high-risk student, with Error_Num contributing 0.36 to this
prediction.
The right chart indicates the actual values of these features. For example, this student made
337 errors while compiling code and executed a total of 2507 programs.
Figure 3 Using LIME to Explain High-Risk Students
This study found inconsistencies in the features used by LIME and SHAP, indicating a
discrepancy in the explanations generated by these two explainers. The detailed features are
shown in Table 4.
Table 4
The Five Most Important Features Explained for the Same High-Risk Student
LIME SHAP
Error_Num Error_Num
Execute_Times Marker_Num
Marker_Operation Marker_Operation
Prev_Next_Operation Code_Paste
Used_Time Prev_Next_Operation
3.3. The quality of Explanation
Table 5 presents the explanation performance results obtained by using logistic regression
and artificial neural networks with two different explainers, LIME and SHAP. It is observed that
when using logistic regression, the explanation quality generated by the LIME explainer is
superior to that of SHAP. On the other hand, when using artificial neural networks, the
explanation quality generated by the SHAP explainer is better than that of LIME.
Table 5
Explanation Quality of Various Explainable AI Algorithms with Different models
Method FA RA PGI PGU RIS ROS
LR+SHAP 0.70 0.62 0.0072 0.0036 3.23 4.69
LR+LIME 0.97 0.72 0.0081 0.0043 0.22 3.68
ANN+SHAP N/A N/A 0.0133 0.0023 0.21 2.43
ANN+LIME N/A N/A 0.0135 0.0026 0.30 3.69
4. Discussion
We noticed in Table 4 that the five most important features are highlighted by LIME and SHAP.
There are three common features, meanwhile, two different features came from each explanation
model. This raises an intriguing perspective that the shared emphasis on these features may
indicate higher importance and reliability.
This viewpoint sparks our interest in delving deeper into the correlation between model
predictive performance and explanatory performance. We plan to further explore this in the
upcoming discussion. This not only enriches our discourse but also contributes to providing a
more comprehensive perspective to address various viewpoints and concerns.
4.1. Methods for Assessing Explanations
To address the first research question regarding the evaluation of explanations from
explainable AI models, we utilized six metrics, as detailed in Section 2.3. The first two metrics,
Feature Agreement (FA) and Rank Agreement (RA), assess fidelity and consistency in
explanations generated by post-hoc models like SHAP or LIME. FA measures the shared top-K
features between the post-hoc explanation and the model's feature-based importance ranking,
while RA evaluates feature ordering consistency. These metrics contribute to understanding the
explainability and trustworthiness of AI systems.
The third and fourth metrics, Prediction Gap on Important Features (PGI) and Prediction Gap
on Unimportant Features (PGU), quantify the impact of perturbations on identified influential and
unimportant features. PGI reflects the alignment between influential features and the model's
prediction, while PGU assesses the model's disregard for unimportant features. These metrics
enhance our understanding of explainability and trustworthiness.
Two additional metrics, Relative Input Stability (RIS) and Relative Output Stability (ROS), were
incorporated. RIS measures changes in the explanation due to slight input modifications,
indicating explanation stability. A smaller RIS value signifies higher stability. Similarly, ROS
quantifies changes in the explanation relative to variations in output probabilities, assessing
explanation robustness. A lower ROS value indicates higher stability. These metrics aim to
comprehensively evaluate the fidelity, alignment, and stability of post-hoc explanations in diverse
AI domains.
4.2. Exploring the Quality of Explanations
Although the accuracy of ANN is lower, we are still interested in seeing how LIME and SHAP
perform on LR and ANN. It is worth noting that six evaluation metrics, including FA, RA, PGI, PGU,
RIS, and ROS, are specifically designed to evaluate explanatory artificial intelligence (XAI) and are
not affected by model accuracy. Therefore, although the accuracy of ANN is lower, it does not
affect our evaluation of LIME and SHAP in terms of interpretation.
From Table 5, it is evident that LIME produces explanations with higher accuracy and
consistency compared to SHAP for the LR model. While LIME exhibits a higher PGU compared to
SHAP, indicating a greater variance in predictions concerning unimportant features, it is essential
to consider other factors in evaluating the overall performance. It is crucial to recognize that PGU
primarily emphasizes the explanation of unimportant features. In this particular experiment, we
emphasize the significance of paying more attention to explanations related to important
features. Additionally, the model's explanatory performance is not solely determined by a single
indicator; instead, a comprehensive assessment considering multiple indicators is necessary.
LIME performance better than SHAP for the LR model can be attributed to the similarity in
functionality between LIME and logistic regression. LIME operates by providing a locally
interpretable model (using linear regression) for complex and opaque models, aiming to find a
simple and understandable model for a specific instance to address the question of "why the
model classifies an instance into a specific category"[22].
Both logistic regression and linear regression employ similar formulas, with the distinction
that logistic regression applies a sigmoid function to transform the regression results into
predicted probabilities, while linear regression does not involve this sigmoid transformation.
This resemblance in approach between logistic regression and LIME explains why LIME performs
better when explaining logistic regression models[18, 22].
On the other hand, SHAP generates explanations with higher fidelity and stability in the ANN
model compared to LIME. This may be attributed to the fact that SHAP's functioning is more
similar to artificial neural networks. SHAP operates by analyzing the explainability of a model's
predictions in terms of the contribution of each feature, calculating the Shapley values for each
feature to measure its impact on the predictions. Higher contribution indicates higher importance
of that feature. However, the training methodology of artificial neural networks involves
transformations through the states of neurons in hidden layers, representing nonlinear
classification[25]. Therefore, linear regression, which is used by LIME, may not provide
explanations of higher quality, leading to SHAP's feature contributions aligning better with the
training methodology of artificial neural networks.
4.3. Resolving Differences in Explanations
Firstly, user feedback can be gathered through methods such as questionnaires or interviews
to assess the quality of explanations. Secondly, in the absence of user or expert input, explanations
can be selected based on their accuracy and stability. If the explanations generated by a specific
explainer demonstrate better stability compared to those produced by other explainers, that
specific explainer can be chosen. For example, when dealing with predictions made by artificial
neural network models, SHAP's explanations outperform LIME in three out of four evaluation
criteria (PGI, PGU, RIS, and ROS). Therefore, SHAP explanations can be employed in such
scenarios. Conversely, LIME's explanations surpass SHAP in five out of six evaluation criteria (FA,
RA, PGI, RIS, and ROS) for logistic regression models. Hence, LIME explanations can be utilized
when working with logistic regression predictions.
5. Conclusion
In conclusion, this study emphasizes the significance of explainable and trustworthy AI in the field
of education. By employing two machine learning methods (logistic regression and neural
networks) and two explainable AI packages (LIME, SHAP), the research evaluates and generates
explanations for students' learning outcomes. The use of six evaluation metrics for explainability
ensures the accuracy and stability of these explanations.
The findings of this study contribute to the development of explainable AI models that are
transparent and can be trusted by education teams. By providing comprehensive explanations
for model predictions, the study enhances the understanding and confidence of stakeholders in
the decision-making process of AI systems. This promotes the responsible and sustainable
integration of artificial intelligence in educational settings. Moreover, the research highlights the
importance of evaluation metrics in assessing the quality and effectiveness of explanations
generated by explainable AI models. Establishing such metrics not only ensures reliable system
performance but also supports the establishment of fundamental principles for building
trustworthy AI in various contexts.
In our future work, we plan to extend our analysis to include diverse model architectures,
which will allow us to highlight differences in particular features and explore the aspects of
importance that transcend specific model structures. This expansion could contribute to a more
robust evaluation of model interpretability and feature importance across various modeling
paradigms.
Acknowledgements
This work is supported by Ministry of Science and Technology, Taiwan under grants MOST-112-
2410-H-008 -019 -MY3, MOST-111-2410-H-008 -010 -MY3, and Ministry of Education, Taiwan.
Lastly, I acknowledge the assistance of ChatGPT in refining the language of this paper.
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