Towards real interpretability of student success prediction combining methods of XAI and social science

Despite calls to increase the focus on explainability and interpretability in EDM and, in particular, student success prediction, so that it becomes useful for personalized intervention systems, only few efforts have been undertaken in that direction so far. In this paper, we argue that this is mainly due to the limitations of current Explainable Artificial Intelligence (XAI) approaches regarding interpretability. We further argue that the issue, thus, calls for a a combination of AI and social science methods utilizing the strengths of both. For this, we introduce a step-wise model of interpretability where the first step constitutes of knowing important features, the second step of understanding counterfactuals regarding a particular person’s prediction, and the third step of uncovering causal relations relevant for a set of similar students. We show that LIME, a current XAI method, reaches the first but not subsequent steps. To reach step two, we propose an extension to LIME, Minimal Counterfactual-LIME, finding the smallest number of changes necessary to change a prediction. Reaching step three, however, is more involved and additionally requires theoretical and causal reasoning - to this end, we construct an easily applicable framework. Using artificial data, we showcase that our methods can recover connections among features; additionally, we demonstrate its applicability on real-life data. Limitations of our methods are discussed and collaborations with social scientists encouraged

When probabilities are not enough - A framework for causal explanations of student success models

Student success and drop-out predictions have gained increased attention in recent years, connected to the hope that by identifying struggling students, it is possible to intervene and provide early help and design programs based on patterns discovered by the models. Though by now many models exist achieving remarkable accuracy-values, models outputting simple probabilities are not enough to achieve these ambitious goals. In this paper, we argue that they can be a first exploratory step of a pipeline aiming to be capable of reaching the mentioned goals. By using Explainable Artificial Intelligence (XAI) methods, such as SHAP and LIME, we can understand what features matter for the model and make the assumption that features important for successful models are also important in real life. By then additionally connecting this with an analysis of counterfactuals and a theory-driven causal analysis, we can begin to reasonably understand not just if a student will struggle but why and provide fitting help. We evaluate the pipeline on an artificial dataset to show that it can, indeed, recover complex causal mechanisms and on a real-life dataset showing the method’s applicability. We further argue that collaborations with social scientists are mutually beneficial in this area but also discuss the potential negative effects of personal intervention systems and call for careful designs

Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?

Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios, it is important that goal recognition algorithms can recognize goals of an observed agent as fast as possible. However, many early approaches in the area of Plan Recognition As Planning, require quite large amounts of computation time to calculate a solution. Mainly to address this issue, recently, Pereira et al. developed an approach that is based on planning landmarks and is much more computationally efficient than previous approaches. However, the approach, as proposed by Pereira et al., also uses trivial landmarks (i.e., facts that are part of the initial state and goal description are landmarks by definition). In this paper, we show that it does not provide any benefit to use landmarks that are part of the initial state in a planning landmark based goal recognition approach. The empirical results show that omitting initial state landmarks for goal recognition improves goal recognition performance.Comment: Will be presented at KI 202

When Probabilities Are Not Enough - A Framework for Causal Explanations of Student Success Models

Student success and drop-out predictions have gained increased attention in recent years, connected to the hope that by identifying struggling students, it is possible to intervene and provide early help and design programs based on patterns discovered by the models. Though by now many models exist achieving remarkable accuracy-values, models outputting simple probabilities are not enough to achieve these ambitious goals. In this paper, we argue that they can be a first exploratory step of a pipeline aiming to be capable of reaching the mentioned goals. By using Explainable Artificial Intelligence (XAI) methods, such as SHAP and LIME, we can understand what features matter for the model and make the assumption that features important for successful models are also important in real life. By then additionally connecting this with an analysis of counterfactuals and a theory-driven causal analysis, we can begin to reasonably understand not just if a student will struggle but why and provide fitting help. We evaluate the pipeline on an artificial dataset to show that it can, indeed, recover complex causal mechanisms and on a real-life dataset showing the method’s applicability. We further argue that collaborations with social scientists are mutually beneficial in this area but also discuss the potential negative effects of personal intervention systems and call for careful designs

Predicting master’s students’ academic performance: An empirical study in Germany

The tremendous growth in electronic educational data creates the need to have meaningful information extracted from it. Educational Data Mining (EDM) is an exciting research area that can reveal valuable knowledge from educational databases. This knowledge can be used for many purposes, including identifying dropouts or weak students who need special attention and discovering extraordinary students who can be offered lifetime opportunities. Although former studies in EDM used an extensive range of features for predicting students’ academic achievement (in terms of (i) achieved grades or (ii) passing and failing), those features are sometimes not obtainable for practical usage, and therefore, the prediction models are not feasible for employment. This study uses data mining (DM) algorithms to predict the academic performance of master’ s students by using a non-extensive data set and including only the features that are easy to collect at the beginning of a studying program. To perform this study, we have collected over 700 students' records from 2010 to 2018 from the Faculty of Business Informatics and Mathematics at the University of Mannheim in Germany. Those records include demographics and post-enrollment features such as semester grades. The empirical results show the following: (i) the most significant features for predicting students' academic achievements are the students’ grades in each semester (importance rate between 14 and 36%), followed by the distance from students’ accommodation to university (importance rate between 6 and 18%) and culture (importance rate between 7 and 17%). On the other hand, gender, age, the numbers of failed courses, and the number of registered and unregistered exams per semester are less significant for the predictions. (ii) As expected, predictions performed after the second semester is more accurate than those performed after the first semester. (iii) Unsurprisingly, models that predict two classes yield better results than those that predict three. (iv) Random Forest classifier performs the best in all prediction models (0.77–0.94 accuracy), and using oversampling methods to deal with imbalanced data can significantly improve the performance of DM methods. For future work, we recommend testing the predictive models on other master programs and a larger datasets. Furthermore, we recommend investigating other oversampling approaches

Plan-similarity based heuristics for goal recognition

Plan and goal recognition are important tasks in constructing smart assistants and can be used to support activity recognition. However, all current approaches have the disadvantage of either being slow and inefficient regarding both online computation time and prior manual modelling effort tackled by human experts due to using domains which makes them infeasible for real-life settings; or requiring large amounts of annotated observation sequences which is not realistic as data annotation is expensive. This paper introduces a new approach requiring neither annotated observation data nor domains but only exemplary plans for each possible goal. It is based on plan-similarity based heuristics which makes it fast, yet is still able to achieve good results. This leads to new possibilities regarding the applicability in real-life settings and increases the usefulness for supporting activity recognition

Investigating the importance of demographic features for EDM-predictions

Demographic features are commonly used in Educational Data Mining (EDM) research to predict at-risk students. Yet, the practice of using demographic features has to be considered extremely problematic due to the data’s sensitive nature, but also because (historic and representation) biases likely exist in the training data, which leads to strong fairness concerns. At the same time and despite the frequent use, the value of demographic features for prediction accuracy remains unclear. In this paper, we systematically investigate the importance of demographic features for at-risk prediction using several publicly available datasets from different countries. We find strong evidence that including demographic features does not lead to better-performing models as long as some study-related features exist, such as performance or activity data. Additionally, we show that models, nonetheless, place importance on these features when they are included in the data – although this is not necessary for accuracy. These findings, together with our discussion, strongly suggest that at-risk prediction should not include demographic features. Our code is available at: https://anonymous.4open.science/r/edm-F7D1

Planning landmark based goal recognition revisited: Does using initial state landmarks make sense?

Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios, it is important that goal recognition algorithms can recognize goals of an observed agent as fast as possible. However, many early approaches in the area of Plan Recognition As Planning, require quite large amounts of computation time to calculate a solution. Mainly to address this issue, recently, Pereira et al. developed an approach that is based on planning landmarks and is much more computationally efficient than previous approaches. However, the approach, as proposed by Pereira et al., also uses trivial landmarks (i.e., facts that are part of the initial state and goal description are landmarks by definition). In this paper, we show that it does not provide any benefit to use landmarks that are part of the initial state in a planning landmark based goal recognition approach. The empirical results show that omitting initial state landmarks for goal recognition improves goal recognition performance

Leveraging planning landmarks for hybrid online goal recognition

Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios it is important that goal recognition algorithms can recognize goals of an observed agent as fast as possible and with minimal domain knowledge. Hence, in this paper, we propose a hybrid method for online goal recognition that combines a symbolic planning landmark based approach and a data-driven goal recognition approach and evaluate it in a real-world cooking scenario. The empirical results show that the proposed method is not only significantly more efficient in terms of computation time than the state-of-the-art but also improves goal recognition performance. Furthermore, we show that the utilized planning landmark based approach, which was so far only evaluated on artificial benchmark domains, achieves also good recognition performance when applied to a real-world cooking scenario.Comment: 9 pages. Presented at SPARK 2022 (https://icaps22.icaps-conference.org/workshops/SPARK/

Morbidity and mortality after anaesthesia in early life: results of the European prospective multicentre observational study, neonate and children audit of anaesthesia practice in Europe (NECTARINE)

Background: Neonates and infants requiring anaesthesia are at risk of physiological instability and complications, but triggers for peri-anaesthetic interventions and associations with subsequent outcome are unknown. Methods: This prospective, observational study recruited patients up to 60 weeks' postmenstrual age undergoing anaesthesia for surgical or diagnostic procedures from 165 centres in 31 European countries between March 2016 and January 2017. The primary aim was to identify thresholds of pre-determined physiological variables that triggered a medical intervention. The secondary aims were to evaluate morbidities, mortality at 30 and 90 days, or both, and associations with critical events. Results: Infants (n=5609) born at mean (standard deviation [sd]) 36.2 (4.4) weeks postmenstrual age (35.7% preterm) underwent 6542 procedures within 63 (48) days of birth. Critical event(s) requiring intervention occurred in 35.2% of cases, mainly hypotension (>30% decrease in blood pressure) or reduced oxygenation (SpO2 <85%). Postmenstrual age influenced the incidence and thresholds for intervention. Risk of critical events was increased by prior neonatal medical conditions, congenital anomalies, or both (relative risk [RR]=1.16; 95% confidence interval [CI], 1.04-1.28) and in those requiring preoperative intensive support (RR=1.27; 95% CI, 1.15-1.41). Additional complications occurred in 16.3% of patients by 30 days, and overall 90-day mortality was 3.2% (95% CI, 2.7-3.7%). Co-occurrence of intraoperative hypotension, hypoxaemia, and anaemia was associated with increased risk of morbidity (RR=3.56; 95% CI, 1.64-7.71) and mortality (RR=19.80; 95% CI, 5.87-66.7). Conclusions: Variability in physiological thresholds that triggered an intervention, and the impact of poor tissue oxygenation on patient's outcome, highlight the need for more standardised perioperative management guidelines for neonates and infants