PENERAPAN MODEL PROBLEM SOLVING LABORATORY UNTUK MENINGKATKAN KEMAMPUAN PEMECAHAN MASALAH SECARA KREATIF DAN PEMAHAMAN KONSEP DI SMA PADA MATERI FLUIDA STATIS

Penelitian ini bertujuan mendapatkan gambaran tentang peningkatan kemampuan pemecahan masalah secara kreatif dan pemahaman konsep kelompok siswa yang mengikuti kegiatan praktikum menggunakan model Problem Solving Laboratory dinadingkan dengan siswa yang mengikuti kegiatan praktikum menggunakan model verification lab. Metode yang digunakan dalam penelitian ini adalah metode quasi eksperiment dengan desain randomized control group pretest posttest design. Subyek penelitian ini adalah para siswa kelas XI salah satu SMA Negeri di Kabupaten Luwu Sulawesi Selatan. Sampel penelitian sebanyak 64 orang siswa yang terbagi dalam dua kelas yaitu kelas eksperimen (32 siswa) dan kelas kontrol (32 siswa). Instrumen yang digunakan untuk mengumpulkan data berupa tes pemahaman konsep dan tes kemampuan pemecahan masalah secara kreatif terkait materi fluida statis. Data yang sudah dikumpulkan selanjutnya dianalisis menggunakan perhitungan N-gain dan uji hipotesis menggunakan uji beda dua rerata N-Gain. Hasil analisis data menunjukkan bahwa implementasi model problem solving lab secara signifikan dapat lebih meningkatkan pemahaman konsep dan kemampuan pemecahan konsep dan kemampuan pemecahan masalah secara kreatif dibanding implementasi model verification lab. ABSTRACT This study aims to figure out an overview of improving creative problem solving skills and concept understanding of groups of students who take part in practical activities using the Problem Solving Laboratory model compared to students who take part in practical activities using the verification lab model. The method used in this study is a quasi-experimental method with a randomized control group pretest posttest design. The subjects of this study were students of class XI of one of the public high schools in Luwu Regency, South Sulawesi. The research sample was 64 students who were divided into two classes, namely the experimental class (32 students) and the control class (32 students). The instrument used to collect data is in the form of a concept understanding test and a creative problem solving ability test related to static fluid material. The data that has been collected is then analyzed using N-gain calculations and hypothesis testing using the difference test of two-mean N-Gain. The results of data analysis show that the implementation of the problem solving lab model can significantly improve conceptual understanding and creative problem solving skills compared to the implementation of the verification lab model

Modeling Learner Mood In Realtime Through Biosensors For Intelligent Tutoring Improvements

Computer-based instructors, just like their human counterparts, should monitor the emotional and cognitive states of their students in order to adapt instructional technique. Doing so requires a model of student state to be available at run time, but this has historically been difficult. Because people are different, generalized models have not been able to be validated. As a person’s cognitive and affective state vary over time of day and seasonally, individualized models have had differing difficulties. The simultaneous creation and execution of an individualized model, in real time, represents the last option for modeling such cognitive and affective states. This dissertation presents and evaluates four differing techniques for the creation of cognitive and affective models that are created on-line and in real time for each individual user as alternatives to generalized models. Each of these techniques involves making predictions and modifications to the model in real time, addressing the real time datastream problems of infinite length, detection of new concepts, and responding to how concepts change over time. Additionally, with the knowledge that a user is physically present, this work investigates the contribution that the occasional direct user query can add to the overall quality of such models. The research described in this dissertation finds that the creation of a reasonable quality affective model is possible with an infinitesimal amount of time and without “ground truth” knowledge of the user, which is shown across three different emotional states. Creation of a cognitive model in the same fashion, however, was not possible via direct AI modeling, even with all of the “ground truth” information available, which is shown across four different cognitive states