# Using LSTM to Identify Help Needs in Primary School Scratch Students
## 1 Introduction  
In the last decade, online learning environments have gained attention, even more so since the start of the lockdowns in 2020 caused by the coronavirus (SARS-CoV2 or COVID-19) pandemic [1]. Additionally, Computer Science (CS) is experiencing exponential growth, and Scratch, a graphical block-based programming language, is a widely-used approach to improving students' Computational Thinking (CT) skills in primary school [2; 3]. In an educational environment, and even more so in primary education, feedback is an important factor, as it allows students to evaluate their progress in learning. Identifying student's mental blocks and providing personalized feedback can be difficult when teachers and learners are separated by space and time, such as in online learning settings [4]. As concluded in [5], automatic feedback provided by the software can improve students' performance in programming activities. How far a student is from finding the solution to an exercise is a key factor in knowing if he/she needs help. For this purpose, there are methods that calculate the distance between the student's workspace and the given solution of the exercise at hand. However, though different existing distance calculation approaches take into account the name andthe position of the program blocks, they ignore other attributes like the block family, or the value of the block inputs. For this reason, in [6], we proposed a novel method for calculating distances in block-based programming languages. One of the most important elements of the learning process is knowing when to ask for help. When a student has exhausted his/her capacity for understanding and still cannot find a solution for the problem he/she is facing, he/she has to ask for help from a more competent person. However, some students avoid asking for help because they think they are incompetent and not smart enough [7]. This can prevent the student from progressing, causing him/her to lose motivation and interest in the subject. Therefore, identifying the moment when a student needs help to complete the task he/she is carrying out is a crucial aspect of effective learning. In Ref. [6], the defined distance between the student's workspace and the solution varies depending on the difficulty of the exercise and the student's knowledge of Scratch. So each student will need help at different points in time. In Ref. [8] it is concluded that investigating the time and circumstances in which a learner requests help in an intelligent tutor system allows us to understand the type of help they need, and therefore provide high-quality hints. However, we did not find in the literature any model for the identification of help needs in block-based programming languages. This leads us to propose a help identification mechanism based on a time-series model and on user interactions in block-based programming languages. In the last few years, time-series models have seen increasing use in forecasting [9] and are widely implemented in education for identifying key elements at a certain moment, for example to analyse the concentration of students during English courses [10], or for predicting school absenteeism of autistic students [11]. Until recently it was not common to integrate ethical considerations into the development processes of artificial intelligence applications. However, nowadays experts agree on the importance of embedding ethical requirements into the design of artificial intelligence systems [12]. One of the key ethical concerns in education is discrimination based on gender and towards minority groups. An educational system that has a bias can cause student frustration and lead to loss of interest in the subject, which can in turn lead to academic failure. In recent years, researchers have focused their efforts on improving the ethical frameworks of artificial intelligence, and implementing them in different areas, including in education.