Contemporary developments in teaching and learning introductory programming: Towards a research proposal

The teaching and learning of introductory programming in tertiary institutions is problematic. Failure rates are high and the inability of students to complete small programming tasks at the completion of introductory units is not unusual. The literature on teaching programming contains many examples of changes in teaching strategies and curricula that have been implemented in an effort to reduce failure rates. This paper analyses contemporary research into the area, and summarises developments in the teaching of introductory programming. It also focuses on areas for future research which will potentially lead to improvements in both the teaching and learning of introductory programming. A graphical representation of the issues from the literature that are covered in the document is provided in the introduction

Incorporating Rich Features into Deep Knowledge Tracing

The desire to follow student learning within intelligent tutoring systems in near real time has led to the development of several models anticipating the correctness of the next item as students work through an assignment. Such models have in- cluded Bayesian Knowledge Tracing (BKT), Performance Factors Analysis (PFA), and more recently with developments in Deep Learning, Deep Knowledge Tracing (DKT). The DKT model, based on the use of a recurrent neural network, exhibited promising results in paper [PBH+15]. Thus far, however, the model has only considered the knowledge components of the problems and correctness as input, neglecting the breadth of other features col- lected by computer-based learning platforms. This work seeks to improve upon the DKT model by incorporating more features at the problem-level and student-level. With this higher dimensional input, an adaption to the original DKT model struc- ture is also proposed, incorporating an Autoencoder network layer to convert the input into a low dimensional feature vector to reduce both the resource requirement and time needed to train. Experimental results show that our adapted DKT model, which includes more combinations of features, can effectively improve accuracy

Knowledge Tracing: A Review of Available Technologies

As a student modeling technique, knowledge tracing is widely used by various intelligent tutoring systems to infer and trace the individual’s knowledge state during the learning process. In recent years, various models were proposed to get accurate and easy-to-interpret results. To make sense of the wide Knowledge tracing (KT) modeling landscape, this paper conducts a systematic review to provide a detailed and nuanced discussion of relevant KT techniques from the perspective of assumptions, data, and algorithms. The results show that most existing KT models consider only a fragment of the assumptions that relate to the knowledge components within items and student’s cognitive process. Almost all types of KT models take “quize data” as input, although it is insufficient to reflect a clear picture of students’ learning process. Dynamic Bayesian network, logistic regression and deep learning are the main algorithms used by various knowledge tracing models. Some open issues are identified based on the analytics of the reviewed works and discussed potential future research directions

Towards Personalized Learning using Counterfactual Inference for Randomized Controlled Trials

Personalized learning considers that the causal effects of a studied learning intervention may differ for the individual student (e.g., maybe girls do better with video hints while boys do better with text hints). To evaluate a learning intervention inside ASSISTments, we run a randomized control trial (RCT) by randomly assigning students into either a control condition or a treatment condition. Making the inference about causal effects of studies interventions is a central problem. Counterfactual inference answers “What if� questions, such as Would this particular student benefit more if the student were given the video hint instead of the text hint when the student cannot solve a problem? . Counterfactual prediction provides a way to estimate the individual treatment effects and helps us to assign the students to a learning intervention which leads to a better learning. A variant of Michael Jordan\u27s Residual Transfer Networks was proposed for the counterfactual inference. The model first uses feed-forward neural networks to learn a balancing representation of students by minimizing the distance between the distributions of the control and the treated populations, and then adopts a residual block to estimate the individual treatment effect. Students in the RCT usually have done a number of problems prior to participating it. Each student has a sequence of actions (performance sequence). We proposed a pipeline to use the performance sequence to improve the performance of counterfactual inference. Since deep learning has achieved a huge amount of success in learning representations from raw logged data, student representations were learned by applying the sequence autoencoder to performance sequences. Then, incorporate these representations into the model for counterfactual inference. Empirical results showed that the representations learned from the sequence autoencoder improved the performance of counterfactual inference

Comparison of Ray Tracing through Ionospheric Models

A comparison of ray tracing predictions for transionospheric electromagnetic wave refraction and group delays through ionospheric models is presented. Impacted applications include over-the-horizon RADAR, high frequency communications, direction finding, and satellite communications. The ionospheric models used are version 2.1 of Utah State University\u27s Global Assimilation of Ionospheric Measurements (USU GAIM) model and the 2001 version of the International Reference Ionosphere (IRI) model. In order to provide ray tracing results applicable to satellite communications for satellites at geosynchronous orbit (GEO), a third ionospheric model is used to extend the sub-2000-km USU GAIM and IRI ionospheric specifications to 36540 km in altitude. The third model is based on an assumption of diffusive equilibrium for ion species above 2000 km. The ray-tracing code used is an updated implementation of the Jones-Stephenson ray tracing algorithm provided by L. J. Nickisch and Mark A. Hausman. Ray tracing predictions of signal refraction and group delay are given for paths between Goldstone Deep Space Observatory near Barstow, California, and the PanAmSat Galaxy 1R satellite. Results are given for varying frequency between 11MHz to 1GHz, varying time of day between 0600 and 1700 Pacific Standard Time on 1 November 2004, and varying signal transmission elevation angle. Ray tracing predicts minimal ionospheric effects on signals at or above approximately 100 MHz. Signals below 100 MHz are predicted to refract on different paths by each model. Ray tracing in diffusive equilibrium extended (DEE) specifications of USU GAIM predicts as much as 500 km less group delay than in DEE IRI. This appears to be due to the diffusive equilibrium extension from typically higher electron densities predicted in the upper altitudes of IRI\u27s specifications