Background: Collaborative programming is important and challenging for K12 students. Scaffolding is a vital method to support students' collaborative programming learning. However, conventional scaffolding that do...
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Background: Collaborative programming is important and challenging for K12 students. Scaffolding is a vital method to support students' collaborative programming learning. However, conventional scaffolding that does not fade may lead students to become overly dependent, resulting in unsatisfactory programming performance. Objectives: This study customises a three-stage fade-out scaffolding in collaborative programming to reduce scaffolding dependence and improve students' programming achievement, self-efficacy and programming behaviour. Methods: A quasi-experimental study lasting 18 weeks was conducted at a middle school. One hundred twenty-one eighth-grade students participated in the study;they were randomly assigned into the experimental group with three-stage fade-out scaffolding (N = 62) and the control group with conventional scaffolding (N = 59). Then, this study adopted an analysis of covariance (ANCOVA), independent samples t test, and lag sequential analysis to analyse the data. Results and ConclusionsThe results indicated that the programming achievement, self-efficacy, and programming behaviour patterns of the students in the experimental group outperformed those of the control group. Additionally, boy-dominated groups display more positive and active programming behaviour patterns than girl-dominated groups. Implications: This study designs a three-stage fade-out scaffolding approach for collaborative programming and provides diverse empirical evidence, offering valuable suggestions for the design of programming instruction and the analysis of learning processes.
Educational research has established that learning can be defined as an enduring change in behaviour, which results from practice or other forms of experience. In introductory programming courses, proficiency is typic...
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ISBN:
(纸本)9781450346986
Educational research has established that learning can be defined as an enduring change in behaviour, which results from practice or other forms of experience. In introductory programming courses, proficiency is typically approximated through relatively small but frequent assignments and tests. Scaling these assessments to track significant behavioural change is challenging due to the subtle and complex metrics that must be collected from large student populations. Based on a four-semester study, we present an analysis of learning tool interaction data collected from 514 students and 38,796 solutions to practice programming exercises. We first evaluate the effectiveness of measuring workflow patterns to detect students at-risk of failure within the first three weeks of the semester. Our early predictor analysis accurately detects 81% of the students who struggle throughout the course. However, our early predictor also captures transient struggling, as 43% of the students who ultimately did well in the course were classified as at-risk. In order to better differentiate sustained versus transient struggling, we further propose a trajectory metric which measures changes in programming behaviour. The trajectory metric detects 70% of the students who exhibit sustained struggling, and mis-classifies only 11% of students who go on to succeed in the course. Overall, our results show how detecting changes in programming behaviour can help us differentiate between learning and struggling in CS1.
Cats were trained to walk on a specially designed treadmill: the cats were able to collect food pellets by switching motor patterns with or without the help of exteroceptive stimuli inherent to the treadmill. To study...
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Cats were trained to walk on a specially designed treadmill: the cats were able to collect food pellets by switching motor patterns with or without the help of exteroceptive stimuli inherent to the treadmill. To study the involvement of the caudate nucleus in switching motor patterns cats received intracaudate bilateral injections of haloperidol. In addition, in a final series of experiments, EMG recordings of 2 antagonistic muscles, together with recordings of characteristic changes in the length of 1 muscle, were made before and after the haloperidol treatment. Haloperidol treatment resulted in a decreased number of motor patterns which were not directed by exteroceptive stimuli (non-exteroceptively directed motor patterns). This haloperidol-induced effect was dose-dependently counteracted by the additional intracaudate injections of apomorphine which per se remained ineffective. Haloperidol neither altered the number of food collecting attempts nor reduced the number of exteroceptively directed motor patterns. Furthermore, haloperidol did not affect the capacity to switch to proprioceptively directed motor patterns. Finally, haloperidol did not produce abnormalities in EMG and length signals recorded from hindlimb muscles. Haloperidol selectively reduced the animals''s capacity to program non-stimulus directed motor behavior. The data are discussed in view of their significance for therapy of patients with basal ganglia disorders, such as patients suffering from Parkinson''s disease.
Encountering difficulties that emerge in programming learning are ubiquitous for novices. Those difficulties may reduce novices' motivation for learning, even lead them to drop out. Automatically detecting the dif...
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Encountering difficulties that emerge in programming learning are ubiquitous for novices. Those difficulties may reduce novices' motivation for learning, even lead them to drop out. Automatically detecting the difficulties has been proved as an effective way to solve this problem. A potential method for the detection is to identify programmers' behavioural patterns based on the sequences of programming events (e.g., editing, deleting, or debugging) when they experience programming difficulties. However, due to the diversity of these events, the plentiful difficulties generated from novices, and the naturally complicated behavioural patterns, accurately detecting the difficulties remains challenging. In this study, we propose a deep representation based on the programming events as the pre-treatment of a recurrent neural network with an attention mechanism to detect programming difficulty. This representation can be conducive to encode the complicated behavioural features (e.g., co-occurrence, sequences, and time intervals of the events) of the difficulty patterns for deep learning models. The results show that the accuracy of our method reaches 94%, which increases by 16% compared with the traditional machine learning model.
programming difficulties are one of the common problems faced by software engineering students,which can lead to a rapid decline in motivation and even drop *** students’programming difficulties is a crucial step in ...
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programming difficulties are one of the common problems faced by software engineering students,which can lead to a rapid decline in motivation and even drop *** students’programming difficulties is a crucial step in understanding their current programming situation and implementing appropriate instructional ***,how to detect students’programming difficulties accurately without students’awareness remains a big *** the issues above;this paper adopts a sensor-free difficulties detecting method based on a deep neural network which employs a recurrent neural network(RNN)model and uses the sequential timing data from programming *** method can detect students’programming difficulties in real-time with 93%accuracy without interference in the programming *** the long term,this method is the first step for establishing an automated intelligent programming *** the same time,it can assist teachers in noticing the difficulties that students ***,teachers can adjust their teaching plans and provide manual tutoring intervention more quickly.
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