IMPROVING STUDENT’S LEARNING OUTCOMES BY USING MICROSOFT EXCEL AND STATISTICAL PROGRAM OF SOCIAL SCIENCE

One of the factors causing the low score of student learning outcomes Statistics and Probability on midterm exams is that during practice or studying on their own, students feel the complexity of solving questions about data analysis in Statistics and Probability courses and the difficulty of obtaining confirmation of correct answers. This encourages to find alternative solutions. One of them is using Excel and Statistical Program of Social Science (SPSS) software in Statistics and Probability learning. The purpose of this research is to improve the learning outcomes of Statistics and Probability using Excel and SPSS software. The method used in this research is the One-Group Pretest-Posttest Design. Samples were selected using the Cluster Random Sampling technique. To see if there is an increase in learning outcomes, it is done by comparing the average learning outcomes of the midterm exams (before using Excel and SPSS software) and final semester exams (after using Excel and SPSS software). The results of the Wicoxon test showed Asymp. Sig. (2-tailed) 0.000 less than 0.05, so it can be concluded that the pretest and posttest means are significantly different. This shows that there is an increase in learning outcomes before and after learning using Excel and SPSS software, so it is concluded that student learning outcomes in Statistics and Probability courses can be improved through learning by using Excel and SPSS software. Keywords: excel; learning outcomes; software; SPSS Abstrak Salah satu faktor penyebab rendahnya nilai hasil belajar Statistik dan Probabilitas mahasiswa pada ujian tengah semester disebabkan pada saat latihan atau belajar sendiri. Mahasiswa merasakan rumitnya penyelesaian soal tentang analisis data pada mata kuliah Statistik dan Probabilitas dan sulitnya memperoleh konfirmasi jawaban benar. Hal ini mendorong untuk menemukan alternatif solusi. Salah satu alternative solusi adalah memanfaatkan software Excel dan Statistical Program of Social Science(SPSS) dalam pembelajaran Statistik dan Probabilitas.Tujuan penelitian ini adalah untuk meningkatkan hasil belajar Statistik dan Probabilitas menggunakan software Excel dan SPSS. Metode yang digunakan dalam penelitian ini adalah One-Group Pretest-Posttest Design. Sampel dipilih dengan teknik Cluster random Sampling. Untuk melihat apakah ada peningkatan hasil belajar, dilakukan dengan membandingkan rata-rata hasil belajar ujian tengah semester (sebelum memanfaatkan software Excel dan SPSS) dan ujian akhir semester (setelah memanfaatkan software Excel dan SPSS). Hasil uji Wicoxon menunjukkan Asymp. Sig. (2-tailed) 0,000 kurang dari 0,05 sehingga disimpulkan bahwa rata-rata pretest dan posttest berbeda secara signifikan. Hal ini menunjukkan bahwa terjadi peningkatan hasil belajar sebelum dan sesudah pembelajaran memanfaatkan software Excel dan SPSS, sehingga disimpulkan bahwa hasil belajar mahasiswa pada mata kuliah Statistik dan Probabilitas dapat ditingkatkan melalui pembelajaran dengan memanfaatkan software Excel dan SPSS.Kata kunci: excel; hasil belajar; software; SPSS.

Simulated central vision loss does not impair implicit location probability learning when participants search through simple displays

Central vision loss disrupts voluntary shifts of spatial attention during visual search. Recently, we reported that a simulated scotoma impaired implicit spatial attention towards regions likely to contain search targets. In that task, search items were overlaid on natural scenes. Because natural scenes can induce explicit awareness of learned biases leading to voluntary shifts of attention, here we used a search display with a blank background less likely to induce awareness of target location probabilities. Participants searched both with and without a simulated central scotoma: a training phase contained targets more often in one screen quadrant and a testing phase contained targets equally often in all quadrants. In Experiment 1, training used no scotoma, while testing alternated between blocks of scotoma and no-scotoma search. Experiment 2 training included the scotoma and testing again alternated between scotoma and no-scotoma search. Response times and saccadic behaviors in both experiments showed attentional biases towards the high-probability target quadrant during scotoma and no-scotoma search. Whereas simulated central vision loss impairs implicitly learned spatial attention in the context of natural scenes, our results show that this may not arise from impairments to the basic mechanisms of attentional learning indexed by visual search tasks without scenes.

Building Relational Understanding through Hypothetical Learning Trajectory of Probability

Many researchers found that students had difficulty in understanding probability material. Students mostly focus on applying formulas to find solutions to problems without knowing what the concept is and why the formula works. This underlies the researcher to design probability learning as a hypothetical learning trajectory. The study aims to describe a series of learning activities designed to build relational understanding skills in probability material. This study uses a design research method consisting of three stages, namely preparation and design, teaching experiment, and retrospective analysis. Data collection techniques were carried out using a video recorder, documentation, and test questions. The data collected is in the form of qualitative data. The collected data is interpreted by peers, teachers, and supervisors to reduce the subjectivity of the researcher's point of view. All data that has been collected were analyzed retrospectively. The results of the research conducted showed that students experienced an increase and gave a good response in solving problems. Teachers are expected to use a learning design with a realistic mathematical approach because it helps students understand learning and apply their knowledge in everyday life.

Location probability learning in 3-dimensional virtual search environments

When a visual search target frequently appears in one target-rich region of space, participants learn to search there first, resulting in faster reaction time when the target appears there than when it appears elsewhere. Most research on this location probability learning (LPL) effect uses 2-dimensional (2D) search environments that are distinct from real-world search contexts, and the few studies on LPL in 3-dimensional (3D) contexts include complex visual cues or foraging tasks and therefore may not tap into the same habit-like learning mechanism as 2D LPL. The present study aimed to establish a baseline evaluation of LPL in controlled 3D search environments using virtual reality. The use of a virtual 3D search environment allowed us to compare LPL for information within a participant’s initial field of view to LPL for information behind participants, outside of the initial field of view. Participants searched for a letter T on the ground among letter Ls in a large virtual space that was devoid of complex visual cues or landmarks. The T appeared in one target-rich quadrant of the floor space on half of the trials during the training phase. The target-rich quadrant appeared in front of half of the participants and behind the other half. LPL was considerably greater in the former condition than in the latter. This reveals an important constraint on LPL in real-world environments and indicates that consistent search patterns and consistent egocentric spatial coding are essential for this form of visual statistical learning in 3D environments.

Rational After All: Changes in Probability Matching Behaviour Across Time in Humans and Monkeys

Probability matching—where subjects given probabilistic in-put respond in a way that is proportional to those input probabilities—has long been thought to be characteristic of primate performance in probability learning tasks in a variety of contexts, from decision making to the learning of linguistic variation in humans. However, such behaviour is puzzling because it is not optimal in a decision theoretic sense; the optimal strategy is to always select the alternative with the highest positive-outcome probability, known as maximising(in decision making) or regularising (in linguistic tasks). While the tendency to probability match seems to depend somewhat on the participants and the task (i.e., infants are less likely to probability match than adults, monkeys probability matchless than humans, and probability matching is less likely in linguistic tasks), existing studies suffer from a range of deficiencies which make it difficult to robustly assess these differences. In this project we present a series of experiments which systematically test the development of probability matching behaviour over time in simple decision making tasks, across species (humans and Guinea baboons), task complexity, and task domain (linguistic vs non-linguistic).