Higher education institutions are increasingly considering the use of a form of blended learning, commonly named as flipped classroom (FC), in which students watch video lectures drawn from a massive online open course (MOOC) before a face-to-face lecture. This methodology is attractive, as it allows institutions to reuse high-quality material developed for MOOCs, while increasing learning flexibility and the students' autonomy. However, the adoption of this methodology is low in general, especially in Engineering courses, as its implementation faces a number of challenges for students. The most salient challenge is the lack of student self-regulatory skills, which may result in frustration and low performance. In this paper, we study how a selfregulatory learning technological scaffold, which provides students with feedback about their activity in the MOOC, affects the engagement and performance of students in an Engineering course following a MOOC-based FC approach. To this end, we design an observational study with the participation of 242 students: 133 students in the experimental group (EG) who used a technological scaffold and 109 in the control group (CG) who did not. We did not find a statistically significant difference between the academic achievements of both groups. However, the EG exhibited a statistically significant greater engagement with the course and a more accurate strategic planning than the CG. The main implications for scaffolding self-regulated learning in FC derived from these results are discussed. K E Y W O R D S flipped classroom, higher education, massive open online course, self-regulation, time management 1 | INTRODUCTION Higher education institutions (HEIs) have been compelled to adapt and transform their education mission, placing innovation at the center of the learning and teaching processes. Such transformations are a response to changes in the educational landscape influenced by various factors, including new regulations (e.g., the European Bologna Process), changes in the demographics of the student population [30,55], PÉREZ-SANAGUSTÍN ET AL.

Many systems for learning programming have been developed in view of learning programming is difficult. Unfortunately, a few tools for learning algorithmic have been developed. In this paper, we present Algo+, a tool based on Information and Communications Technology (ICT), to assess algorithmic competencies. Its pedagogical aim is purchasing algorithmic competencies, analyzing ability and refinement steps strategy to resolve a problem. Algo+ gives a problem to learner and let him/her resolve it graphically in terms of basic and elementary operations (operation well known in algorithmic) using the refinement steps strategy. After constructing solution, the last one is submitted to, not only a formative and diagnostic assessment (a formative feedback) but also a summative assessment. The role of this tool is to assess algorithmic skills and to make an evolutionary base of pedagogical errors for reusing it to enhancing the student's problem solving abilities.

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