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During the past decades, researches about automatic grading have become an interesting issue. These studies focuses on how to make machines are able to help human on assessing students' learning outcomes. Automatic grading enables teachers to assess student's answers with more objective, consistent, and faster. Especially for essay model, it has two different types, i.e. long essay and short answer. Almost of the previous researches merely developed automatic essay grading (AEG) instead of automatic short answer grading (ASAG). This study aims to assess the sentence similarity of short answer to the questions and answers in Indonesian without any language semantic's tool. This research uses pre-processing steps consisting of case folding, tokenization, stemming, and stopword removal. The proposed approach is a scoring rubric obtained by measuring the similarity of sentences using the stringbased similarity methods and the keyword matching process. The dataset used in this study consists of 7 questions, 34 alternative reference answers and 224 student's answers. The experiment results show that the proposed approach is able to achieve a correlation value between 0.65419 up to 0.66383 at Pearson's correlation, with Mean Absolute Error (𝑀𝐴𝐸) value about 0.94994 until 1.24295. The proposed approach also leverages the correlation value and decreases the error value in each method.

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Parallax scrolling technique is being devoted as a unique and an innovative trend in the web design. Parallax scrolling provides 3D perception on a web page. Previous works observed user experience issues of parallax scrolling merely based on subjective questionnaires. Their findings leave a research question whether the results are valid, as participants may perceive a written questionnaire differently. Additionally, bias and ambiguity in the questionnaire can affect the research results significantly. To solve this research problem, we present a novel user experience study of parallax scrolling in storytelling and online shop website using eye tracking and User Experience Questionnaire (UEQ). Forty (N=40) participant joined the experiment on a voluntary basis. Each participant only interacted with one out of two websites (storytelling or online shop) and only one effect (with or without parallax scrolling). We found that parallax scrolling affected UEQ score of Attractiveness of the storytelling website (p < 0.05). Our findings suggest that parallax scrolling improves user engagement in storytelling website. We also observed that the participants spent time almost two times faster to find an object of interest in an online shop with parallax scrolling compared with the similar task in an online shop without parallax scrolling (p < 0.05). We thus argue that parallax scrolling is useful during interacting with particular websites that require visual object localization. In future, web designers should consider the appropriate usage of parallax scrolling to optimize user experience while avoiding additional distraction caused by this technique.

The use of digital data now saves a lot of information, but still raw and not in the form of knowledge that can be directly seen, therefore we need a data processing to get useful particular understanding from raw data. Data Mining has an important role to process and find useful information from data. This process is also called Knowledge Discovery. Educational Data Mining is a Knowledge Discovery process in the world of education using data mining techniques. One method used in data mining is clustering. By using clustering analysis techniques, data can be grouped into groups without the need for prior knowledge (previous knowledge), so that the data is grouped based on similarity of patterns. This paper compares the K-means, Hierarchical and Louvain clustering methods to see the most appropriate clustering technique in analyzing log activity data in Moodle Learning Management System (LMS). The results of clustering are measured using the Silhouette Coefficient, and then we compare the values and distribution between clusters. In conclusion, Hierarchical clustering produces the highest Silhouette Coefficient value and also this algorithm can detect outlier data as new cluster. Louvain clustering perform very well to find cluster groups in new dataset as the algorithm does not required the number of clusters to be specified before. Louvain clustering can divide more evenly and precision compare to K-means and hierarchical cluster techniques.

For higher education institutions that encourage digital transformation, understanding the barriers are necessary for the digital transformation accomplishment. The purpose of this paper is to present a review of the literature on barriers to digital transformation in higher education. To get a wide overview in identifying the barriers to the implementation of digital transformation, a structured literature review was used to select the relevant studies published. Nine categories were identified based on the literature reviewed: vision, strategy and policy, resources, leadership, digital skill and knowledge, technology, adaptability, resistance to change, and government and economic. Our findings provided a fish-bone diagram that outlines twenty-two barriers to digital transformation in higher education. The main contribution of this study is a synthesis of the state of the art of barriers to digital transformation in higher education. We contribute to provide a common basic understanding of barriers to digital transformation in higher education to overcome barriers for improving the possibility of success. Moreover, we give an insight into future research on barriers exploration

Abstract-A learning style is an issue related to learners. In one way or the other, learning styles could assist learners in their learning activities. If the learners ignore their learning styles, it may influence their effort in understanding teaching materials. To overcome these problems, a model for reliable automatic learning style detection is needed. Currently, there are two approaches in automatically detecting learning styles: data driven and literature based. Learners, especially those with changing learning styles, have difficulties in adopting these two approaches since they are not adaptive, dynamic and responsive (ADR). To solve the above problems, a model using agent learning approach is proposed. Agent learning performs four phased activities, i.e. initialization, learning, matching and recommendations to decide which learning styles are used by the students. Furthermore, the system will provide teaching materials which are appropriate for the detected learning style. The detection process is performed automatically by combining data-driven and literature-based approaches. The detected learning style used for this research is VARK (Visual, Auditory, Read/Write, and Kinesthetic). This learning style detection model is expected to optimize the learners in adhering with the online learning.Index Terms-detection model, VARK, reinforcement learning.

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