The effect of immediate feedback on mathematics learning achievement

Real facts at school show that the ability to solve problems is still considered low. Most students still make many mistakes in solving math problems. These mistakes must be directed to the right path to minimize repeated mistakes. Immediate feedback can be a solution to correct these errors. The purpose of this study was to examine the immediate feedback by the teacher during learning and their relationship to student learning achievement. The participants in this study consisted of 30 students from seventh grade. Data were collected from the final score on the number of subjects. Data were analyzed using descriptive statistics and paired sample t-tests to describe before dan after giving immediate feedback. Paired sample t-test analysis was also used to describe the relationship between immediate feedback on learning achievement. This study revealed a significant difference between immediate feedback and learning achievement. The final mean value shows the pretest average is higher than the posttest average. There is a positive relationship between immediate feedback and mathematics learning achievement. These findings can be used as a teacher's attention in providing feedback immediately during learning

The effect of immediate feedback on mathematics learning achievement

Real facts at school show that the ability to solve problems is still considered low. Most students still make many mistakes in solving math problems. These mistakes must be directed to the right path to minimize repeated mistakes. Immediate feedback can be a solution to correct these errors. The purpose of this study was to examine the immediate feedback by the teacher during learning and their relationship to student learning achievement. The participants in this study consisted of 30 students from seventh grade. Data were collected from the final score on the number of subjects. Data were analyzed using descriptive statistics and paired sample t-tests to describe before dan after giving immediate feedback. Paired sample t-test analysis was also used to describe the relationship between immediate feedback on learning achievement. This study revealed a significant difference between immediate feedback and learning achievement. The final mean value shows the pretest average is higher than the posttest average. There is a positive relationship between immediate feedback and mathematics learning achievement. These findings can be used as a teacher's attention in providing feedback immediately during learning

Optimasi pembobotan pada query expansion dengan Term Relatedness to Query-Entropy based (TRQE)

Pemilihan term yang tepat untuk memperluas queri merupakan hal yang penting pada query expansion. Oleh karena itu, perlu dilakukan optimasi penentuan term yang sesuai sehingga mampu meningkatkan performa query expansion pada system temu kembali dokumen. Penelitian ini mengajukan metode Term Relatedness to Query-Entropy based (TRQE), sebuah metode untuk mengoptimasi pembobotan pada query expansion dengan memperhatikan aspek semantic dan statistic dari penilaian relevansi suatu pseudo feedback sehingga mampu meningkatkan performa temukembali dokumen. Metode yang diusulkan memiliki 3 modul utama yaitu relevan feedback, pseudo feedback, dan document retrieval. TRQE diimplementasikan pada modul pseudo feedback untuk optimasi pembobotan term pada ekspansi query. Evaluasi hasil uji coba menunjukkan bahwa metode TRQE dapat melakukan temukembali dokumen dengan hasil terbaik pada precision 100% dan recall sebesar 22,22%.Metode TRQE untuk optimasi pembobotan pada query expansion terbukti memberikan pengaruh untuk meningkatkan relevansi pencarian dokumen

Enhancing passage retrieval in log files by query expansion based on explicit and pseudo relevance feedback

International audiencePassage retrieval is usually defined as the task of searching for passages which may contain the answer for a given query. While these approaches are very efficient when dealing with texts, applied to log files (i.e. semi-structured data containing both numerical and symbolic information) they usually provide irrelevant or useless results. Nevertheless one appealing way for improving the results could be to consider query expansions that aim at adding automatically or semi-automatically additional information in the query to improve the reliability and accuracy of the returned results. In this paper, we present a new approach for enhancing the relevancy of queries during a passage retrieval in log files. It is based on two relevance feedback steps. In the first one, we determine the explicit relevance feedback by identifying the context of the requested information within a learning process. The second step is a new kind of pseudo relevance feedback. Based on a novel term weighting measure it aims at assigning a weight to terms according to their relatedness to queries. This measure, called TRQ (Term Relatedness to Query), is used to identify the most relevant expansion terms. The main advantage of our approach is that is can be applied both on log files and documents from general domains. Experiments conducted on real data from logs and documents show that our query expansion protocol enables retrieval of relevant passages

Query Expansion Pada LINE TODAY Dengan Algoritme Extended Rocchio Relevance Feedback

LINE TODAY memberikan akses informasi berupa konten-konten berita up to date. Data pada LINE TODAY dimanfaatkan untuk dapat dilakukan fitur pencarian berita. Teknik Query Expansion akan sangat berguna jika dikombinasikan dengan sistem pencarian, sebab query yang diinputkan pengguna akan dikombinasi dengan query tambahan yang diberikan oleh sistem. Query tambahan akan membuat query yang pengguna hasilkan lebih spesifik. Selain itu, hadirnya feedback pengguna (user judgement/explicit relevance feedback) yang melakukan penilaian pada tiap berita akan meminimalisir query yang ambigu. Proses yang dilakukan diawali dengan teknik preprocessing, yang terdiri dari beberapa tahapan, yaitu cleansing, case folding, tokenization, filtering, hingga stemming. Kemudian dilakukan pembobotan term (term weighting) dan cosine similarity. Setelah itu, proses yang dilakukan ialah perhitungan dengan metode Extended Rocchio Relevance Feedback yang merupakan metode turunan dari Rocchio Relevance Feedback, untuk menghasilkan query tambahan. Hasil yang diperoleh berdasarkan dari implementasi maupun pengujian pada penelitian Query Expansion pada LINE TODAY dengan Algoritme Extended Rocchio Relevance Feedback menghasilkan rata-rata nilai Precision sebesar 0.53308, Recall sebesar 0.81708, F-Measure sebesar 0.59553, dan Akurasi sebesar 0.9574. Nilai akurasi yang dida pat dengan metode Extended Rocchio Relevance Feedback berdasar user judgement cenderung meningkat hingga 2% dibandingkan pencarian otomatis dengan metode Rocchio Relevance Feedback

Synsets improve short text clustering for search support: combining LDA and WordNet

In this study, I proposed a short text clustering approach with WordNet as the external resources to cluster documents from corpus.byu.edu. Experimental results show that our approach largely improved the clustering performance. The factors that have an influence on the performance of the topic model are the total number of documents, Synsets distribution among topics and words overlapping between the query’s Synsets. In addition, the performance will also be influenced by the missing Synset in WordNet. Finally, we provide an idea of using clustering approaches generating ranked query suggestion to disambiguate the query. Combining with Synsets of the query, text document clustering can provide an effective way to disambiguate user search query by organizing a large set of searching results into a small number of groups labeled with Synsets from WordNet.Master of Science in Information Scienc