Natural Learning Systems Research Articles

Why death and aging ? All memories are imperfect

Recent papers have emphasized the primary role of cellular information management in biological and evolutionary development. In this framework, intelligent cells collectively measure environmental cues to improve informational validity to support natural cellular engineering as collaborative decision-making and problem-solving in confrontation with environmental stresses. These collective actions are crucially dependent on cell-based memories as acquired patterns of response to environmental stressors. Notably, in a cellular self-referential framework, all biological information is ambiguous. This conditional requirement imposes a previously unexplored derivative. All cellular memories are imperfect. From this atypical background, a novel theory of aging and death is proposed. Since cellular decision-making is memory-dependent and biology is a continuous natural learning system, the accumulation of previously acquired imperfect memories eventually overwhelms the flexibility cells require to react adroitly to contemporaneous stresses to support continued cellular homeorhetic balance. The result is a gradual breakdown of the critical ability to efficiently measure environmental information and effect cell-cell communication. This age-dependent accretion governs senescence, ultimately ending in death as an organism-wide failure of cellular networking. This approach to aging and death is compatible with all prior theories. Each earlier approach illuminates different pertinent cellular signatures of this ongoing, obliged, living process.

CHALLENGES IN THE IMPLEMENTATION OF INSTRUCTIONAL STRATEGY MODEL BASED ON THE BRAIN NATURAL LEARNING SYSTEMS FOR INCLUSIVE CLASSROOM

One of the problems teachers often face in inclusive classrooms is the difficulty of designing effective and flexible instruction that can meet the needs of all students. This study explores general teacher (GT) opinions on the challenges in implementing instructional strategies based on the natural brain learning systems for inclusive classrooms in elementary schools. Data were collected through classroom observations and semi-str One of the problems teachers often face in inclusive classrooms is the difficulty of designing effective and flexible instruction that can meet the needs of all students. This study explores general teacher (GT) opinions on the challenges in implementing instructional strategies based on the natural brain learning systems for inclusive classrooms in elementary schools. Data were collected through classroom observations and semi-structured interviews with seven GTs who have implemented instructional strategies based on the natural brain learning system in an inclusive classroom. Data were analyzed using thematic analysis. The research has four main themes: modification of teaching materials, modification of instructional methods, modification of instructional media, and time. The challenge in implementing instructional strategies based on the natural learning system of the brain lies in the readiness of GTs in facing all changes in the instructional process that GTs has implemented. This challenge will expect to be the experience of GTs in designing more effective learning that must fully support all parties to provide meaningful learning for all students. uctured interviews with seven GTs who have implemented instructional strategies based on the natural brain learning system in an inclusive classroom. Data were analyzed using thematic analysis. The research has four main themes: modification of teaching materials, modification of instructional methods, modification of instructional media, and time. The challenge in implementing instructional strategies based on the natural learning system of the brain lies in the readiness of GTs in facing all changes in the instructional process that GTs has implemented. This challenge will expect to be the experience of GTs in designing more effective learning that must fully support all parties to provide meaningful learning for all students.

Guru di Era Digital: Pelatihan Implementasi Schoology sebagai Learning Management System bagi Guru SMP Veteran Kota Cirebon

TEACHERS IN THE DIGITAL ERA: TRAINING ON THE IMPLEMENTATION OF SCHOOLOGY AS A LEARNING MANAGEMENT SYSTEM FOR TEACHERS OF SMP VETERAN CIREBON CITY. Distance education is a natural learning system that involves two-way communication through the media between educators and students when there is a distance that separates them. To integrate the two-way communication process requires a learning management system (Learning Management System). One of the LMS platforms is Schoology. Schoology allows users to create, manage, and interact with each other and share academic content. Schoology provides solutions for teachers to improve their professionalism in the digital age, especially in the use of technology for learning. The resource person trains participants with the lecture method, which explains how to use the menus and tools contained in LMS Schoology, as well as simulations of how to implement them in learning. The training stage consists of the preparation phase and the implementation phase of the training. The target of the training is the teachers of all subjects in Cirebon Veterans Junior High School. Indicators of training implementation with evaluation during the training process and post-training evaluation. From the results of the community service activities, during the face-to-face events, the teachers looked enthusiastic, only when the exercises were online (non-face-to-face), only one teacher actively participated out of a total of 9 teachers who became training participants. Based on observations, few teachers took part in online activities because of their busy operations in classroom teaching, which had to pursue productive weeks of learning.

The Benefits of Implementation of an Instructional Strategy Model Based on the Brain's Natural Learning Systems in Inclusive Classrooms in Higher Education

Instructional strategy in inclusive classrooms in higher education has not yet been able to accommodate the needs and competencies that must be achieved by students who have various characteristics, learning styles and different obstacles. The aim of this research is to explore the perceptions of student’s teacher toward the implementation of an instructional strategy model based on the brain’s natural learning system, especially its benefits for all students in inclusive classrooms in higher education. Data was col-lected through open observation and interviews on students to determine the benefits of an instructional strategy model based on the natural learning system of the brain. The data were analyzed using qualitative data analysis. The research produced four major themes consisting of emotional skills, so-cial skills, cognitive skills, self-development. The findings have implica-tions for lecturers, as well as inclusive education researchers in higher edu-cation that focus on learning in inclusive classrooms by using an instruc-tional strategy model based on the brain’s natural learning system as a suit-able instructional strategy model to be carried out in inclusive classrooms in higher education.

Design of Instructional Strategy Model Based on the Brain's Natural Learning System (MS-SiPAO) in Inclusive Classrooms in Higher Education

One of the problems faced by lecturers in achieving instructional objectives in inclusive classrooms in higher education is that they have not been able to design effective instructional strategies that are in accordance with all characteristics of all students. This study aims to determine the instructional strategy model that is based on a student problem and needs analysis. The study involved sixty elementary school teacher students at one university in Bogor, Indonesia. Data collection was conducted using questionnaires and open interviews. Meanwhile, data analysis used a qualitative data analysis model. The results showed that the instructional strategy model based on the brain's natural learning system (MS-SiPAO) is one of the instructional strategy models that are expected to accommodate all the needs of students in order to achieve the competencies and instructional objectives set by the lecturer. Finally, MS-SiPAO is a suitable model to be used because it is based on an analysis of the problems and analysis of the needs faced by students in implementing learning in inclusive classrooms in higher education.

Model brain based learning (BBL) and whole brain teaching (WBT) in learning

<p class="Abstract">The learning process is a process of change in behavior as a form of the result of learning. The learning model is a crucial component of the success of the learning process. The learning model is growing fastly, and each model has different characteristics. Teachers are required to be able to understand each model to teach the students optimally by matching the materials and the learning model. The best of the learning model is the model that based on the brain system in learning that are the model of Brain Based Learning (BBL) and the model of Whole Brain Teaching (WBT). The purposes of this article are to obtain information related to (1) the brain’s natural learning system, (2) analyze the characteristics of the model BBL and WBT based on theory, brain sections that play a role associated with syntax, similarities, and differences, (3) explain the distinctive characteristics of both models in comparison to other models. The results of this study are: (1) the brain’s natural learning system are: (a) the nerves in each hemisphere do not work independently, (b) doing more activities can connect more brain nerves, (c) the right hemisphere controls the left side motoric sensor of the body, and vice versa; (2) the characteristics of BBL and WBT are: (a) BBL is based on the brain’s structure and function, while the model WBT is based on the instructional approach, neurolinguistic, and body language, (b) the parts of the brain that work in BBL are: cerebellum, cerebral cortex, frontal lobe, limbic system, and prefrontal cortex; whereas the parts that work WBT are: prefrontal cortex, visual cortex, motor cortex, limbic system, and amygdala, (c) the similarities between them are that they both rely on the brain’s system and they both promote gesture in learning, whereas the differences are on the view of the purposes of gestures and the learning theory that they rely on. BBL relies on cognitive theory while WBT relies on social theory; (3) the typical attribute of them compared to other models are that in BBL there are classical music and gestures in the form of easy exercises, while on the WBT model there are fast instructions and movements as instructions or code of every spoken word.</p>

Model Pelaksanaan Pembelajaran Pendidikan Agama Islam dengan Sistem Moving Class Dalam Meningkatkan Motivasi dan Prestasi Belajar Siswa SMP IT Sahabat Alam

Moving classes is teaching and learning systems that provide a new atmosphere in the learning process. This system is characterized by the moved class where students will move in accordance with the scheduled subjects. The concept of moving class refers to a student-centered learning and provides a dynamic environment in accordance with what they learn. It is very influential to the development of education in an effort to improve the quality of human life. The results of this study showed that according to the writer�s observation and study, SMP IT Sahabat Alam has not implemented yet the learning system that based on moving class learning that requires it has its own classroom space for each subject. But the school has its own unique characteristics and the learning system is to be based on a natural learning system that is suitable to the material. In addition, SMP IT Sahabat Alam has several activities that are of Islamic education include learning the Qur�an, Qur�an Night, Tadabur Alam, all of them still have a link to the learning of Islamic education that is done out of the class and class hours.

Architectural constraints on learning and memory function

Recent experimental and computational studies have identified relationships between architecture and functional performance in information processing systems ranging from natural neuronal ensembles [1,2] to artificial neural networks [3,4]. While these systems can vary greatly in their size and complexity, they share certain structural features, such as parallel and layered motifs [5]. Quantifying how these features influence functionality is a first step toward understanding the behavior of both natural and artificial information processing systems. Of particular interest is the impact of structural architecture on the ability of the system to balance stability with flexibility, for example in memory versus learning. In this study, we use neural networks as model information processing systems to examine tradeoffs in learning and memory processes arising from variations in structural organization. We compare the performance of parallel and layered structures during sequential function approximation, a task that requires networks to produce, retain, and dynamically adapt representations of external information. We measure network performance over a range of learning conditions by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time allowed for learning. By characterizing local error landscape curvature, we can directly relate the functional performance of the system to its underlying architecture. Across a range of both parallel and layered system architectures, we find that variations in error landscape curvature give rise to tradeoffs between the ability of these networks to learn new versus retain old information, maximize success versus minimize failure, and produce specific versus generalizable representations of information. In particular, parallel networks generate smooth error landscapes with deep, narrow minima. Therefore, given sufficient time and through the adjustment of a large number of connection weights, parallel networks can find highly specific representations of the external information. Although accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations by adjusting a fewer number of weights. Although less accurate, these representations are more easily adaptable. We have conducted a detailed analysis of network performance over a range of parallel and layered architectures, thereby isolating learning and memory tradeoffs that arise from underlying structural complexity. A thorough understanding of small network systems is crucial for predicting the behavior of larger systems in which statistical studies of performance would not be possible. In particular, these results may provide insight into the behavior of composite systems, such as cortical layers composed of structurally distinct columns [6] or modular divide-and-conquer networks [7], which share features of both parallel and layered architectures. Additionally, the existence of tradeoffs inherent to a range of network structures may help explain the variability of architectural motifs observed in large-scale biological [5] and technical [3] systems. Identifying the structural mechanisms that impact performance has implications for understanding a wide variety of both natural and artificial learning systems.

Liturgical Spiritual Formation Across the Generations

Click to increase image sizeClick to decrease image size Notes Gabriel Moran, Showing How: The Act of Teaching (Valley Forge, PA: Trinity Press International, 1997), 37. Presbyterian Church (U.S.A.), Office of Spiritual Formation, Louisville, KY. Available at http://www.pcusa.org/spiritualformation/whatis.pdf. For resources for spiritual formation see Peter Feldmeier, The Developing Christian: Spiritual Growth through the Life Cycle (New York: Paulist Press, 1989); David F. White, Practicing Discernment with Youth: A Transformative Youth Ministry Approach (Cleveland, OH: Pilgrim Press, 2005); Gordon W. Lathrop, The Pastor: A Spirituality (Minneapolis: Fortress Press, 2006); Patricia Hendricks, Hungry Souls, Holy Companions: Mentoring a New Generation of Christians (Harrisburg, PA: Morehouse Publishing, 2006); Jim Wilhoit, Spiritual Formation As If the Church Mattered: Growing in Christ Through Community (Grand Rapids, MI: Baker Academic, 2008); Sarah Arthur, The God-Hungry Imagination: The Art of Storytelling for Postmodern Youth Ministry (Nashville, TN: Upper Room Press, 2007); Kenda Creasy Dean and Ron Foster, The Godbearing Life: Soul Tending for Youth Ministry (Nashville, TN: Upper Room Press, 1998). See also the Companions in Christ series from Upper Room Press; Michael J. Anthony, ed., Perspectives on Children's Spiritual Formation: Four Views (Nashville, TN: Broadman & Holman Publishers Academic, 2006); Catherine Stonehouse, Joining Children on the Spiritual Journey: Nurturing the Life of Faith (Grand Rapids, MI: Baker Books, 1998); Mike King, Presence-Centered Youth Ministry: Guiding Students into Spiritual Formation (Downer's Grove, IL: IVP Books, 2006); Robert Keeley, Helping Our Children Grow Spiritually: How Churches Can Nurture Spiritual Development for Kids (Grand Rapids, MI: Baker Books, 2008); Linda Graybeal and Julia L. Roller, Connecting with God: A Spiritual Formation Guide, a Renovaré resource (San Francisco: Harper SanFrancisco, 2006). See also other Renovaré resources published under the direction of Richard Foster by Harper Collins. Marva J. Dawn, A Royal “Waste of Time”: The Splendor of Worshiping God and Being the Church for the World (Grand Rapids, MI: Eerdmans, 1999), 13. Carolyn C. Brown, You Can Preach to the Kids, Too!: Designing Sermons for Adults and Children (Nashville, TN: Abingdon, 1997). Moran, 51. See Eliot Eisner, “The Three Curricula All Schools Teach,” The Educational Imagination: On the Design and Evaluation of School Programs, 3rd ed. (New York: Macmillan College Publishing, 1994). David Elkind, Children and Adolescents: Interpretive Essays on Jean Piaget (New York: Oxford University Press, 1970), 99. Moran, 42. Barbara K. Given, Teaching to the Brain's Natural Learning Systems (Alexandria, VA: Association for Supervision and Curriculum Development, 2002). Given, 5. Daniel Goleman, Emotional Intelligence: Why It Can Matter More than IQ (New York: Bantam Books, 1995). Quoted in Given, 15. Given, 37. Given, 58. Given, 48. See Marianne H. Micks, The Future Present: The Phenomenon of Christian Worship (New York: Seabury, 1970); Donald Saliers, Worship as Theology: Foretaste of Glory Divine (Nashville, TN: Abingdon, 1994). Given, 84. Given, 79. Given, 61. Given, 82. Given, 86. Given, 107. Given, 108–9. Given, 10. In an intriguing exploration of neuroscience and Christian theology, Carol Rausch Albright and James B. Ashbrook make the claim that the very shape of human creaturehood, as understood through neuroscientific discoveries, reflects the nature of God. See Carol Rausch Albright and James B. Ashbrook, Where God Lives in the Human Brain (Naperville, IL: Sourcebooks, 2001). Given, 106, quoting David Perkins, Outsmarting IQ: The Emerging Science of Learnable Intelligence (New York: Free Press, 1995), 117.

Addiction as a Computational Process Gone Awry

Addictive drugs have been hypothesized to access the same neurophysiological mechanisms as natural learning systems. These natural learning systems can be modeled through temporal-difference reinforcement learning (TDRL), which requires a reward-error signal that has been hypothesized to be carried by dopamine. TDRL learns to predict reward by driving that reward-error signal to zero. By adding a noncompensable drug-induced dopamine increase to a TDRL model, a computational model of addiction is constructed that over-selects actions leading to drug receipt. The model provides an explanation for important aspects of the addiction literature and provides a theoretic view-point with which to address other aspects.

Support vector machines and Kernel methods: the new generation of learning machines

Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, statistics, and functional analysis to achieve maximal generality, flexibility, and performance. These algorithms are different from earlier techniques used in machine learning in many respects: For example, they are explicitly based on a theoretical model of learning rather than on loose analogies with natural learning systems or other heuristics. They come with theoretical guarantees about their performance and have a modular design that makes it possible to separately implement and analyze their components. They are not affected by the problem of local minima because their training amounts to convex optimization. In the last decade, a sizable community of theoreticians and practitioners has formed around these methods, and a number of practical applications have been realized. Although the research is not concluded, already now kernel methods are considered the state of the art in several machine learning tasks. Their ease of use, theoretical appeal, and remarkable performance have made them the system of choice for many learning problems. Successful applications range from text categorization to handwriting recognition to classification of gene-expression data.

Computational learning theory and natural learning systems; volume III: Selecting good models

Computational learning theory and natural learning systems; volume III: Selecting good models

The flow of energy, natural learning systems and the creation of life on Earth

The flow of energy, natural learning systems and the creation of life on Earth