scholarly journals HELP-DKT: An Interpretable Cognitive Model of How Students Learn Programming Based on Deep Knowledge Tracing

2021 ◽  
Author(s):  
Yu Liang ◽  
Tianhao Peng ◽  
Yanjun Pu ◽  
Wenjun Wu
Keyword(s):  
Learning Experience ◽  
Cognitive Model ◽  
Prediction Performance ◽  
Good Prediction ◽  
Binary Responses ◽  
Deep Knowledge ◽  
Future Performance ◽  
Good Prediction Performance ◽  
Knowledge Tracing ◽  
Vector Representations

Abstract Student cognitive models are playing an essential role in intelligent online tutoring for programming courses. These models capture students' learning interactions and store them in the form of a set of binary responses, thereby failing to utilize rich educational information in the learning process. Moreover, the recent development of these models has been focused on improving the prediction performance and tended to adopt deep neural networks in building the end-to-end prediction frameworks. Although this approach can provide an improved prediction performance, it may also cause difficulties in interpreting the student's learning status, which is crucial for providing personalized educational feedback. To address this problem, this paper provides an interpretable cognitive model named HELP-DKT, which can infer how students learn programming based on deep knowledge tracing. HELP-DKT has two major advantages. First, it implements a feature-rich input layer, where the raw codes of students are encoded to vector representations, and the error classifications as concept indicators are incorporated. Second, it can infer meaningful estimation of student abilities while reliably predicting future performance. The experiments confirm that HELP-DKT can achieve good prediction performance and present reasonable interpretability of student skills improvement. In practice, HELP-DKT can personalize the learning experience of novice learners.

scholarly journals Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia esculenta (L.) Schott)

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1804
Author(s):  
John Ndisya ◽  
Ayub Gitau ◽  
Duncan Mbuge ◽  
Arman Arefi ◽  
Liliana Bădulescu ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Prediction Models ◽  
Prediction Performance ◽  
Good Prediction ◽  
Hot Air Drying ◽  
Full Spectrum ◽  
Air Drying ◽  
Hot Air ◽  
Good Prediction Performance ◽  
Hue Angle

In this study, hyperspectral imaging (HSI) and chemometrics were implemented to develop prediction models for moisture, colour, chemical and structural attributes of purple-speckled cocoyam slices subjected to hot-air drying. Since HSI systems are costly and computationally demanding, the selection of a narrow band of wavelengths can enable the utilisation of simpler multispectral systems. In this study, 19 optimal wavelengths in the spectral range 400–1700 nm were selected using PLS-BETA and PLS-VIP feature selection methods. Prediction models for the studied quality attributes were developed from the 19 wavelengths. Excellent prediction performance (RMSEP < 2.0, r2P > 0.90, RPDP > 3.5) was obtained for MC, RR, VS and aw. Good prediction performance (RMSEP < 8.0, r2P = 0.70–0.90, RPDP > 2.0) was obtained for PC, BI, CIELAB b*, chroma, TFC, TAA and hue angle. Additionally, PPA and WI were also predicted successfully. An assessment of the agreement between predictions from the non-invasive hyperspectral imaging technique and experimental results from the routine laboratory methods established the potential of the HSI technique to replace or be used interchangeably with laboratory measurements. Additionally, a comparison of full-spectrum model results and the reduced models demonstrated the potential replacement of HSI with simpler imaging systems.

Accumulation of Corresponding Colours Under Extreme Chromatic Illuminations and modification of CAM16

2021 ◽  
Vol 2021 (29) ◽  
pp. 368-373
Author(s):  
Yuechen Zhu ◽  
Ming Ronnier Luo
Keyword(s):  
Magnitude Estimation ◽  
Estimation Method ◽  
Prediction Performance ◽  
Chromatic Adaptation ◽  
Good Prediction ◽  
Lighting Conditions ◽  
Good Prediction Performance ◽  
The Matrix ◽  
D Values ◽  
Magnitude Estimation Method

The goal of this study was to investigate the chromatic adaptation under extreme chromatic lighting conditions using the magnitude estimation method. The locations of the lightings on CIE1976 u′v′ plane were close to the spectrum locus, so the colour purity was far beyond the previous studies, and the data could test the limitations of the existing models. Two psychophysical experiments were carried out, and 1,470 estimations of corresponding colours were accumulated. The results showed that CAT16 gave a good prediction performance for all the chromatic lightings except for blue lighting, and the degree of adaptation was relatively high, that is, D was close to 1. The prediction for blue lightings was modified, the results showed the performance of CAM16 could be improved by correcting the matrix instead of the D values.

scholarly journals An Experimental Evaluation of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs

2021 ◽  
Vol 11 (5) ◽  
pp. 1-18
Author(s):  
Md Kamrul Islam ◽  
Sabeur Aridhi ◽  
Malika Smail-Tabbone
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Black Box ◽  
Prediction Performance ◽  
Prediction Methods ◽  
Good Prediction ◽  
Good Prediction Performance ◽  
Node Similarity ◽  
Category Comparison ◽  
Selection Of

The task of inferring missing links or predicting future ones in a graph based on its current structure is referred to as link prediction. Link prediction methods that are based on pairwise node similarity are well-established approaches in the literature and show good prediction performance in many realworld graphs though they are heuristic. On the other hand, graph embedding approaches learn lowdimensional representation of nodes in graph and are capable of capturing inherent graph features, and thus support the subsequent link prediction task in graph. This paper studies a selection of methods from both categories on several benchmark (homogeneous) graphs with different properties from various domains. Beyond the intra and inter category comparison of the performances of the methods, our aim is also to uncover interesting connections between Graph Neural Network(GNN)- based methods and heuristic ones as a means to alleviate the black-box well-known limitation.

scholarly journals An Experimental Evaluation of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs

2021 ◽  
Vol 11 (05) ◽  
pp. 1-18
Author(s):  
Md Kamrul Islam ◽  
Sabeur Aridhi ◽  
Malika Smail-Tabbone
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Black Box ◽  
Prediction Performance ◽  
Prediction Methods ◽  
Good Prediction ◽  
Good Prediction Performance ◽  
Node Similarity ◽  
Category Comparison ◽  
Selection Of

The task of inferring missing links or predicting future ones in a graph based on its current structure is referred to as link prediction. Link prediction methods that are based on pairwise node similarity are well-established approaches in the literature and show good prediction performance in many realworld graphs though they are heuristic. On the other hand, graph embedding approaches learn lowdimensional representation of nodes in graph and are capable of capturing inherent graph features, and thus support the subsequent link prediction task in graph. This paper studies a selection of methods from both categories on several benchmark (homogeneous) graphs with different properties from various domains. Beyond the intra and inter category comparison of the performances of the methods, our aim is also to uncover interesting connections between Graph Neural Network(GNN)- based methods and heuristic ones as a means to alleviate the black-box well-known limitation.

STOCHASTICALLY EQUIVALENT DYNAMICAL SYSTEM APPROACH TO NONLINEAR DETERMINISTIC PREDICTION

2006 ◽  
Vol 16 (09) ◽  
pp. 2721-2728
Author(s):  
IKUO MATSUBA ◽  
HIROSHI TAKAHASHI ◽  
SHINYA WAKASA
Keyword(s):  
Dynamical System ◽  
Present Method ◽  
Chaotic Systems ◽  
Deterministic Chaos ◽  
Prediction Method ◽  
Prediction Performance ◽  
Good Prediction ◽  
Random Term ◽  
Good Prediction Performance ◽  
Better Than

We propose a new prediction method for nonlinear time series based on the paradigm of deterministic chaos. Introducing a stochastically equivalent dynamical system to an original map, a prediction method is derived by minimizing a random term that defines intervals in which a good prediction performance is obtained. The use of the present method is illustrated for some chaotic systems with particular emphasis on issues of choices of variable time steps that are necessary when discretizing the stochastic differential equation. Applying to some systems, it is found that the present method works better than traditional chaotic methods.

scholarly journals Incorporating Rich Features into Deep Knowledge Tracing

2017 ◽  
Author(s):  
Liang Zhang ◽  
Xiaolu Xiong ◽  
Siyuan Zhao ◽  
Anthony Botelho ◽  
Neil T. Heffernan
Keyword(s):  
Deep Knowledge ◽  
Knowledge Tracing

scholarly journals Nomogram based on homogeneous and heterogeneous associated factors for predicting distant metastases in patients with colorectal cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Tianwen Luo ◽  
Yutong Wang ◽  
Xuefeng Shan ◽  
Ye Bai ◽  
Chun Huang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Lung Metastases ◽  
Roc Curves ◽  
Distant Metastases ◽  
Good Prediction ◽  
Operating Characteristics ◽  
Good Prediction Performance ◽  
Different Types ◽  
Associated Risk Factors

Abstract Background The identification of the homogeneous and heterogeneous risk factors for different types of metastases in colorectal cancer (CRC) may shed light on the aetiology and help individualize prophylactic treatment. The present study characterized the incidence differences and identified the homogeneous and heterogeneous risk factors associated with distant metastases in CRC. Methods CRC patients registered in the SEER database between 2010 and 2016 were included in this study. Logistic regression was used to analyse homogeneous and heterogeneous risk factors for the occurrence of different types of metastases. Nomograms were constructed to predict the risk for developing metastases, and the performance was quantitatively assessed using the receiver operating characteristics (ROC) curve and calibration curve. Results A total of 204,595 eligible CRC patients were included in our study, and 17.07% of them had distant metastases. The overall incidences of liver metastases, lung metastases, bone metastases, and brain metastases were 15.34%, 5.22%, 1.26%, and 0.29%, respectively. The incidence of distant metastases differed by age, gender, and the original CRC sites. Poorly differentiated grade, more lymphatic metastasis, higher carcinoembryonic antigen (CEA), and different metastatic organs were all positively associated with four patterns of metastases. In contrast, age, sex, race, insurance status, position, and T stage were heterogeneously associated with metastases. The calibration and ROC curves exhibited good performance for predicting distant metastases. Conclusions The incidence of distant metastases in CRC exhibited distinct differences, and the patients had homogeneous and heterogeneous associated risk factors. Although limited risk factors were included in the present study, the established nomogram showed good prediction performance.

JKT: A Joint Graph Convolutional Network based Deep Knowledge Tracing

2021 ◽  
Author(s):  
Xiangyu Song ◽  
Jianxin Li ◽  
Yifu Tang ◽  
Taige Zhao ◽  
Yunliang Chen ◽  
...  
Keyword(s):  
Convolutional Network ◽  
Deep Knowledge ◽  
Knowledge Tracing

Federated Deep Knowledge Tracing

2021 ◽  
Author(s):  
Jinze Wu ◽  
Zhenya Huang ◽  
Qi Liu ◽  
Defu Lian ◽  
Hao Wang ◽  
...  
Keyword(s):  
Deep Knowledge ◽  
Knowledge Tracing
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