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The purpose of this paper is to analyze how sequence learning can build on pattern‐recognition systems and how it can contribute to the behavioral options of goal‐oriented systems.

A functional approach is used to develop the necessary cybernetic structures of a subsystem for sequence learning, that can recognize patterns, register patterns occurring repeatedly and connect these to sequences. Based on that it is analyzed how goal‐oriented systems can use information about reoccurring sequences.

A subsystem for sequence learning basically requires pattern recognition and it needs a structure for the directed connection of single standards for pattern matching to standards for sequences, given that it can learn both new patterns and new sequences. Such a subsystem for sequence learning may recognize a certain pattern and with that the end of a certain sequence. So it may deliver more than one output signal at a point in time, and therefore needs additionally a subsystem for directing attention.

The paper analyses the principles of an “associative” way of connecting standards for pattern matching to standards for sequences. Also it shows the cybernetic necessity of an attention directing system that has to decide how to deal with the multiple outputs of a subsystem for sequence learning, i.e. to decide to act either towards a pattern or a whole sequence.

The paper investigates basic mechanisms of sequence learning and its contribution to goal‐oriented behavior. Also, it lays the base for an analysis of attention directing systems and anticipatory systems.

The existing traditional recommendations based on content-based filtering (CBF), collaborative filtering (CF) and hybrid approaches are inadequate for recommending practice challenges in programming online judge (POJ). These systems only consider the preferences of the target users or similar users to recommend items. In the learning environment, recommender systems should consider the learning path, knowledge level and ability of the learner. Another major problem in POJ is the learners don't give ratings to practice challenges like e-commerce and video streaming portals. This purpose of the proposed approach is to overcome the abovementioned shortcomings.

To achieve the context-aware practice challenge recommendation, the data preparation techniques including implicit rating extraction, data preprocessing to remove outliers, sequence-based learner clustering and utility sequence pattern mining approaches are used in the proposed approach. The approach ensures that the recommender system considers the knowledge level, learning path and learning goals of the learner to recommend practice challenges.

Experiments on practice challenge recommendations conducted using real-world POJ dataset show that the proposed system outperforms other traditional approaches. The experiment also demonstrates that the proposed system is recommending challenges based on the learner's current context. The implicit rating extracted using the proposed approach works accurately in the recommender system.

The proposed system contains the following novel approaches to address the lack of rating and context-aware recommendations. The mathematical model was used to extract ratings from learner submissions. The statistical approach was used in data preprocessing. The sequence similarity-based learner clustering was used in transition matrix. Utilizing the rating as a utility in the USPAN algorithm provides useful insights into learner–challenge relationships.

The purpose of the paper is to analyze cybernetic necessities of output‐side attention directing systems, i.e. how systems can decide to act towards one of various inputs.

Complex pattern recognition and sequence learning systems may recognize more than one pattern and deliver more than one output at a point in time. Therefore, they require an output‐side attention directing system to decide to act towards just one pattern. The necessary cybernetic structures of such systems are analyzed using a functional approach.

An output‐side attention directing system has to evaluate the effect of current observations (patterns, sequences, etc.) on highest level goal‐values (in a living system these are existential goal‐values like a body temperature or energy supply). Measure of this effect is the degree of goal‐approximation towards these goal‐values. This measure can either be preprogrammed for some patterns or sequences, or has to be determined in trial and error processes for new patterns or sequences learned by the system.

The paper shows the cybernetic necessities of the development of the “know how” of sequence learning systems in time, starting with default behavior, via learning new patterns and sequences, and trial and error to develop goal‐orientated actions towards them, until finally the achieved results enable experience based directing of attention.

The paper shows basic cybernetic structures and functions for output‐side attention directing systems required for all complex pattern recognition and sequence learning systems.

The purpose of this paper is to analyze how elementary anticipation, understood as anticipation of the repetition of one known pattern, can emerge out of sequence learning and how it can contribute to the behavioral options of goal‐oriented systems.

A functional approach is used to develop the necessary cybernetic structures of a subsystem for sequence learning that can additionally provide standards of anticipated patterns for future pattern matching. Based on that it is analyzed, how a goal‐oriented system can use the information about the actual occurrence of an anticipated pattern.

A subsystem for elementary anticipation of single patterns builds on sequence learning and requires additionally a structure: first, to unequivocally identify the beginning of known sequences just from their first patterns; and second, to decide to use a latter pattern of such a sequence as standard for an anticipated pattern. Deciding to actually use such a pattern for anticipation requires an additional subsystem to switch between the feedback pattern recognition mode and feedforward. Then the occurrence of such an anticipated pattern allows immediate recognition and action.

The paper shows a necessary evolution of cybernetic structures from pattern recognition via sequence learning to anticipation; and it shows, too, a necessary evolution in the cognitive development of individual systems. In the simple anticipatory structures analyzed here, only known patterns, that are part of a known sequence, can become anticipated patterns.

The paper places elementary anticipation of single patterns in an evolutionary development based on pattern recognition and sequence learning. It provides the base to analyze more complex forms of anticipation.

To explain how reading, rewinding a story in reverse order, and then rereading allows a reader to contextualize information, acquiring not only major themes and events but also details and other literacy characteristics of the literature selection.

A representation of sequencing structures is discussed including world-related, concept-related, inquiry-related, learning-related, and utilization-related. In addition, the instructional design aspects of backwards sequencing are discussed.

Just as a level or stud finder uses a back-and-forth approach for finding the most suitable position, so does the backwards sequential approach to reading comprehension. By slowing down and focusing on parts before the whole, students are more likely comprehend content.

The importance of prediction towards comprehension has been recognized for decades. However, using a learning design that features reading a story once, then revisiting the story structure components in backwards order, and finally reading it again, allows for precise and complete learning. This theory has research and pedagogical implications for students of all ages.

In this paper, the authors aim to propose a method for learning robotic assembly sequences, where precedence constraints and object relative size and location constraints can be learned by demonstration and autonomous robot exploration.

To successfully plan the operations involved in assembly tasks, the planner needs to know the constraints of the desired task. In this paper, the authors propose a methodology for learning such constraints by demonstration and autonomous exploration. The learning of precedence constraints and object relative size and location constraints, which are needed to construct a planner for automated assembly, were investigated. In the developed system, the learning of symbolic constraints is integrated with low-level control algorithms, which is essential to enable active robot learning.

The authors demonstrated that the proposed reasoning algorithms can be used to learn previously unknown assembly constraints that are needed to implement a planner for automated assembly. Cranfield benchmark, which is a standardized benchmark for testing algorithms for robot assembly, was used to evaluate the proposed approaches. The authors evaluated the learning performance both in simulation and on a real robot.

The authors' approach reduces the amount of programming that is needed to set up new assembly cells and consequently the overall set up time when new products are introduced into the workcell.

In this paper, the authors propose a new approach for learning assembly constraints based on programming by demonstration and active robot exploration to reduce the computational complexity of the underlying search problems. The authors developed algorithms for success/failure detection of assembly operations based on the comparison of expected signals (forces and torques, positions and orientations of the assembly parts) with the actual signals sensed by a robot. In this manner, all precedence and object size and location constraints can be learned, thereby providing the necessary input for the optimal planning of the entire assembly process.

The purpose of this paper is to identify the different sequence generation techniques for learning, which are applied to a broad category of personalized learning experiences. The papers have been classified using different attributes, such as the techniques used for sequence generation, attributes used for sequence generation; whether the learner is profiled automatically or manually; and whether the path generated is dynamic or static.

The search for terms learning sequence generation and E-learning produced thousands of results. The results were filtered, and a few questions were answered before including them in the review. Papers published only after 2005 were included in the review.

The findings of the paper were: most of the systems generated non-adaptive paths. Systems asked the learners to manually enter their attributes. The systems used one or a maximum of two learner attributes for path generation.

The review pointed out the importance and benefits of learning sequence generation systems. The problems in existing systems and future areas of research were identified which will help future researchers to pursue research in this area.

In the past, there have been several attempts to produce a machine which is able to learn sequences. The early attempts of Uttley and Taylor involved sequence recognition, and are valuable in that they define the problems involved in this type of approach. More recently Fukushima has described a system which reproduces the sequences that are presented to it, and has shown that this approach overcomes many of the difficulties involved in the early work. The present paper describes a system which uses the reproduction approach, but which does not involve the assumption that sequence reproduction must involve delays. Instead a learning theorem is adopted which used interaction between short‐term and long‐term memories, and which therefore obviates the need for complex systems of delay elements. The sensitivity to sequences is inherent in the learning theorem. Simulation of the system on a digital computer has shown that it can learn to reproduce any combination of the ten inputs in sequences of up to eight different elements.

This study considers online searching by health information consumers as a learning process. We focus on search sequences, query reformulation, and conceptual changes.

A qualitative user study (30 participants; three health information seeking tasks) investigated mobile searching behavior. Recorded screen activity, questionnaires, and in-depth personal interview data were collected and analyzed.

(1) Search platform sequences of health information consumers in search as a learning process were exacted and their features were highlighted. (2) Query sequence and reformulation pattern of health information consumers were exacted and discussed. (3) The types and degree of conceptual changes of health consumers were reflected by their query reformulation behavior and differ from different health information search tasks. (4) Characteristics of health consumers' search as learning process were revealed.

(1) A novel perspective of consumer health information studies was proposed by exacting search platform sequence, query sequence and linking them with conceptual changes during the search as learning process. (2) Conceptual changes in the searching as a learning process are regarded as a measure of search outcome in this study, in which terms extracted from queries were used to reflect conceptual changes in consumers' mind. (3) Our findings provide evidences that types of health information seeking tasks do have significant influences on the search as a learning process.

The findings of this study can lead to the fit-to-needs of the search platforms, provide advice for information architecture of search list of search platforms, and guide the design of knowledge graph of health information systems.

Potential relationships between information-seeking behavior and conceptual changes in search as a learning process relative to health information were revealed.

This paper aims to examine how distinct sequences of organizational learning types (experiential and vicarious) underpin processes of exploratory versus exploitative innovation.

Data collection consists of 16 interviews conducted with senior personnel at two firms in the biopharmaceutical sector, with sequences of organizational learning types derived from the associated innovation projects. These sequences and their differential emphases on experiential or vicarious learning are used to construct a conceptual model. Propositions describe the structural differentiation and integration mechanisms useful to foster organizational ambidexterity.

Technological brokering emerges as a key means by which organizations can reconcile the learning sequences underlying exploration and exploitation. For exploration, a structure incorporating cross-industry technology brokerage during the initiation and development phases of innovation is posited. For exploitation, a structure harnessing intra-industry technology brokerage during the development phase of innovation is suggested. Integration of these projects can be accomplished through cross-unit interfaces incorporating both types of brokerage roles, with emphasis on their use during implementation.

This paper considers the ways in which organizations focus on separate types of organizational learning at different stages of the innovation process. Insights are provided into how firms mobilize internal and external knowledge to advance these projects independently, as well as to link these efforts and thereby facilitate ambidexterity.