PENGEMBANGAN MEDIA PEMBELAJARAN BIOLOGI BERBASIS POWTOON PADA MATERI SEL

AbstrakPendidikan pada bangku persekolahan terkadang tidak bisa diterima secara totalitas oleh semua peserta didik, dikarenakan peserta didik mempunyai gaya belajar masing-masing sehingga memunculkan sebuah permasalahan dalam proses pembelajarannya, khususnya seorang guru kadang dalam proses mengajar di kelas masih sering menggunakan media papan tulis dan metode ceramah hingga membuat peserta didik merasa bosan dan tidak mampu memahami materi yang disampaikan oleh gurunya. Penelitian ini bertujuan untuk mengembangkan media pembelajaran biologi berbasis PowToon yang valid, praktis, dan efektif. Penelitian ini mengadaptasi model pengembangan 4-D yang meliputi empat tahap yakni define (pendefenisian), design (perancangan), develop (pengembangan), dan disseminate (penyebaran). Berdasarkan hasil penelitian, diketahui tingkat kevalidan media pembelajaran biologi berbasis PowToon berada pada kategori sangat valid dengan nilai rerata sebesar 3,66. Tingkat kepraktisan media pembelajaran biologi berbasis PowToon berada pada kategori sangat praktis dengan nilai rerata sebesar 3,53. Hasil tes peserta didik dikategorikan sangat efektif karena mencapai rata-rata hasil belajar 83,54 dengan interval >80). Sehingga media pembelajaran biologi berbasis PowToon layak digunakan karena memenuhi tiga kriteria yakni kevalidan, kepraktisan serta keefektifan. Media pembelajaran biologi berbasis PowToon diharapkan dapat diimplementasikan pada kegiatan pembelajaran di kelas. AbstractEducation in school is sometimes not acceptable in totality by all learners, because learners have their own learning style so that it raises a problem in the learning process, especially a teacher sometimes in the process of teaching in the classroom still often uses whiteboard media and lecture methods to make learners feel bored and unable to capture the material delivered by their teachers. This research aims to develop a valid, practical, and effective PowToon-based biological learning medium. This research adapts the 4-D development model which includes four stages namely define (defining), design (design), develop dessiminate. Based on the results of the study, it is known the the level of validity of the PowToon based biology learning media is in the valid category and the mean value is 3.66. The level of practicality of the PowToon based biology learning media is in the very practical category with an average value of 3.53 student test results are categorized as very effective because they achieve an average learning outcome of 83.54 with intervals (>80). So that the PowToon based biology learning media is feasible to use because it meets three criteria, namely validity, practicality and effectiveness. PowToon based biology learning media is expected to be implemented in classroom learning activities.

Multi-timescale biological learning algorithms train spiking neuronal network motor control

Biological learning operates at multiple interlocking timescales, from long evolutionary stretches down to the relatively short time span of an individual's life. While each process has been simulated individually as a basic learning algorithm in the context of spiking neuronal networks (SNNs), the integration of the two has remained limited. In this study, we first train SNNs separately using individual model learning using spike-timing dependent reinforcement learning (STDP-RL) and evolutionary (EVOL) learning algorithms to solve the CartPole reinforcement learning (RL) control problem. We then develop an interleaved algorithm inspired by biological evolution that combines the EVOL and STDP-RL learning in sequence. We use the NEURON simulator with NetPyNE to create an SNN interfaced with the CartPole environment from OpenAI's Gym. In CartPole, the goal is to balance a vertical pole by moving left/right on a 1-D plane. Our SNN contains multiple populations of neurons organized in three layers: sensory layer, association/hidden layer, and motor layer, where neurons are connected by excitatory (AMPA/NMDA) and inhibitory (GABA) synapses. Association and motor layers contain one excitatory (E) population and two inhibitory (I) populations with different synaptic time constants. Each neuron is an event-based integrate-and-fire model with plastic connections between excitatory neurons. In our SNN, the environment activates sensory neurons tuned to specific features of the game state. We split the motor population into subsets representing each movement choice. The subset with more spiking over an interval determines the action. During STDP-RL, we supply intermediary evaluations (reward/punishment) of each action by judging the effectiveness of a move (e.g., moving the CartPole to a balanced position). During EVOL, updates consist of adding together many random perturbations of the connection weights. Each set of random perturbations is weighted by the total episodic reward it achieves when applied independently. We evaluate the performance of each algorithm after training and through the creation of sensory/motor action maps that delineate the network's transformation of sensory inputs into higher-order representations and eventual motor decisions. Both EVOL and STDP-RL training produce SNNs capable of moving the cart left and right and keeping the pole vertical. Compared to the STDP-RL and EVOL algorithms operating on their own, our interleaved training paradigm produced enhanced robustness in performance, with different strategies revealed through analysis of the sensory/motor mappings. Analysis of synaptic weight matrices also shows distributed vs clustered representations after the EVOL and STDP-RL algorithms, respectively. These weight differences also manifest as diffuse vs synchronized firing patterns. Our modeling opens up new capabilities for SNNs in RL and could serve as a testbed for neurobiologists aiming to understand multi-timescale learning mechanisms and dynamics in neuronal circuits.

Beyond gradients: Noise correlations control Hebbian plasticity to shape credit assignment

Two key problems that span biological and industrial neural network research are how networks can be trained to generalize well and to minimize destructive interference between tasks. Both hinge on credit assignment, the targeting of specific network weights for change. In artificial networks, credit assignment is typically governed by gradient descent. Biological learning is thus often analyzed as a means to approximate gradients. We take the complementary perspective that biological learning rules likely confer advantages when they aren't gradient approximations. Further, we hypothesized that noise correlations, often considered detrimental, could usefully shape this learning. Indeed, we show that noise and three-factor plasticity interact to compute directional derivatives of reward, which can improve generalization, robustness to interference, and multi-task learning. This interaction also provides a method for routing learning quasi-independently of activity and connectivity, and demonstrates how biologically inspired inductive biases can be fruitfully embedded in learning algorithms.

The Effects of Conductance on Metastable Switches in Memristive Devices Based on Anti-Hebbian and Hebbian (AHaH) Learning Rules

In the last few years, the literature conferred a great interest in studying the feasibility of using memristive devices for computing. Memristive devices are important in structure, dynamics, as well as functionalities of artificial neural networks (ANNs) because of their resemblance to biological learning in synapses and neurons regarding switching characteristics of their resistance. Memristive architecture consists of a number of metastable switches (MSSs). Although the literature covered a variety of memristive applications for general purpose computations, the effect of low or high conductance of each MSS was unclear. This paper focuses on finding a potential criterion to calculate the conductance of each MMS rather than the whole conductance as reported in the literature. Anti-Hebbian and Hebbian (AHaH) learning rules are used to mimic the changes in memristance of the memristors. This research will concentrate on the effect of conductance on an individual MSS to simulate the nanotechnology devices of the memristors. A single synapse is presented by a couple of memristors to mimic its resistance switching. The learning circuit of artificial synapses could be used in many applications, such as image processing and neural networks, for pattern classification of synapses, represented by a map of the memeristors. These synapses are essential elements for data processing and information storage in both real and artificial neural systems.

An All-in-One Bio-inspired Neural Network

In spite of recent advancements in artificial neural networks (ANNs), the energy efficiency, multifunctionality, adaptability, and integrated nature of the biological neural networks largely remain unimitated in hardware neuromorphic computing systems. Here we exploit optoelectronic, computing, and programmable memory devices based on emerging two-dimensional (2D) layered materials such as MoS2 to demonstrate a monolithically integrated, multi-pixel, and “all-in-one” bio-inspired neural network (BNN) capable of sensing, encoding, learning, forgetting, and inferring at miniscule energy expenditure. We also demonstrate learning adaptability and stimulate learning challenges under specific synaptic conditions to mimic biological learning. Our findings highlight the potential of in-memory computing and sensing based on emerging 2D materials, devices, and integrated circuits not only to overcome the bottleneck of von Neumann computing in conventional CMOS designs but also aid in eliminating peripheral components necessary for competing technologies such as memristors.

The Relationship between Multiple Intelligences and Biology Learning Outcomes of Student

The achievement of learning objectives can be seen from the learning outcomes obtained by students after the learning process. Appropriate learning strategies by paying attention to multiple intelligences in students can affect learning outcomes. This research aims to analyze the relationship of intelligence learning outcomes with the student's biological learning outcomes. This research was conducted on 1st-grade students of natural sciences at Padang High School. The sampling technique is stratified using purposive sampling and proportional random sampling. The research instrument used multiple intelligence questionnaires and multiple-choice questions. The data in this study were analyzed using the Spearman rank correlation test. The results showed that each multiple intelligences had a significant relationship with student learning outcomes, except for three types of intelligence: visual-spatial, physical kinesthetic, and intrapersonal intelligence

Omnidirectional Transfer for Quasilinear Lifelong Learning

In biological learning, data are used to improve performance not only on the current task, but also on previously encountered and as yet unencountered tasks. In contrast, classical machine learning starts from a blank slate, or tabula rasa, using data only for the single task at hand. While typical transfer learning algorithms can improve performance on future tasks, their performance on prior tasks degrades upon learning new tasks (called catastrophic forgetting). Many recent approaches for continual or lifelong learning have attempted to maintain performance given new tasks. But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data. We propose omnidirectional transfer learning algorithms, which includes two special cases of interest: decision forests and deep networks. Our key insight is the development of the omni-voter layer, which ensembles representations learned independently on all tasks to jointly decide how to proceed on any given new data point, thereby improving performance on both past and future tasks. Our algorithms demonstrate omnidirectional transfer in a variety of simulated and real data scenarios, including tabular data, image data, spoken data, and adversarial tasks. Moreover, they do so with quasilinear space and time complexity.

Development of E-Book Learning Media Based on Problem Solving in the Material of the Human Digestive System

: This research aims to develop e-book learning media using the professional Flip pdf application based on problem solving in human digestive system materials and to test the feasibility of e-book media developed through validation of Experts and to find out the effectiveness of teacher and student responses. Is a type of research development / Research and development that uses 4D models with stages (define, design, develop and desiminate) in this study only up to the stage of develope.descriptive qualitative research design. E-book as a free variable and human digestive system as a bound variable. The population in the study was 18 students of class XI AL-khairiyah High School 4 Cilegon, school year 2021/2022. Data retrieval and collection techniques are carried out by spreading questionnaires and interviews, data analysis techniques using interpretase scales and likert scales to test the feasibility of e-books that have been created. The results of the assessment of media experts, obtained a score of 82% and and expert test material obtained a value of 79% which falls into the category of feasible while the response test of the student response test obtained a score of 88% and the teacher board 81% with a very interesting category, based on the results of the study can be concluded that: e-book human suppression system based on problem solving is suitable for student use in biological learning system materials. Keywords:biological learning, e-book, human digestive system, problem solving

ANALISIS LEMBAR KERJA PESERTA DIDIK (LKPD) SEBAGAI BAHAN AJAR BIOLOGI

This research aims to determine the utilization and suitability of the contents of Students Worksheets as a biology teaching material XI IPA class with the curriculum 2013 at SMAN 3 Jember year lesson 2018/2019. Utilization of Students Worksheets to determine the process of drafting SW, the implementation of biological learning using SW. The suitability of LKPD content is analyzed based on display, material, linguistic and question. This research is a qualitative descriptive research. Data analysis is done by calculating the percentage of students Worksheets conformity components. The results of this research for the students worksheet which is compiled sui table using various methods of learning. Results of conformity student workshe ets with the curriculum 2013 class XI IPA at SMAN 3 Jember has a level of conformity is still less in accordance with the standards set by Permendikbud number 8 year 2016 unit that is the view section Includes the Skins section, the beginning of the book, the content, and the end gets 83.3%. There are still two basic competencies that are not listed on the studente worksheet. The average linguistic section gets a value of 80%. The question section gets an average value of 66.6%. Keyword: curriculum 2013, student worksheet conformity, student worksheets, teaching materials Penelitian ini bertujuan untuk mengetahui pemanfaatan dan kesesuaian isi Lembar Kerja Peserta Didik (LKPD) sebagai bahan ajar biologi kelas XI IPA menggunakan kurikulum 2013 di SMAN 3 Jember tahun pelajaran 2018/2019. Pemanfaatan LKPD untuk mengetahui proses penyusunan LKPD, pelaksanaan pembelajaran biologi menggunakan LKPD, dan evaluasi pembelajaran. Kesesuaian isi LKPD dianalisis berdasarkan tampilan, materi, kebahasaan dan soal. Penelitian ini merupakan penelitian kualitatif deskriptif. Analisis data dilakukan dengan menghitung persentase komponen kesesuaian LKPD. Hasil penelitian ini bahwa LKPD yang disusun dapat digunakan dengan berbagai metode pembelajaran. Hasil kesesuaian LKPD dengan kurikulum 2013 kelas XI IPA di SMAN 3 Jember memiliki tingkat kesesuaian masih kurang sesuai dengan standar yang ditetapkan oleh Permendikbud nomor 8 Tahun 2016 tentang buku yang digunakan oleh satuan pendidikan yaitu bagian tampilan meliputi bagian kulit, awal buku, isi, dan bagian akhir. Bagian penyajian yaitu 83,3%. Bagian materi masih terdapat dua Kompetensi Dasar yang belum tercantum pada LKPD yaitu bab sistem reproduksi dan sistem imun. Bagian kebahasaan rata-rata 80%. Bagian soal mendapatkan nilai rata-rata 66,6%. Hal ini menunjukkan bahwa LKPD biologi kelas XI IPA di SMAN 3 Jember masih kurang baik dalam segi penyusunannya akan tetapi layak digunakan karena soal yang disusun guru mengajak siswa untuk berpikir kritis. Kata Kunci: bahan ajar, lembar kerja peserta didik, kesesuaian lembar kerja peserta didik, kurikulum 2013

WORKSHOP APPLICATION OF EXPERIMENTAL METHODS IN BIOLOGICAL LEARNING FOR MADRASAH TEACHERS IN WEST LOMBOK

There are several learning models that are applied in learning Biology in secondary schools. The teaching and learning process in several schools is still dominated by the lecture method. This community service activity aims to improve the knowledge and skills of Madrasah Teachers in West Lombok Regency in applying experimental methods for learning biology on the subject of photosynthesis. The methods used in community service activities are: lectures and discussions, practice photosynthesis experiments, and independent assignments in the form of making learning media. This community service activity was attended by 20 participants consisting of Madrasah teachers from West Lombok Regency. This activity received a positive response from the target participants. This activity has succeeded in increasing participants' knowledge and skills regarding the subject of photosynthesis and experimental methods for learning photosynthesis.